first

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,50 @@
+name: "Bug Report"
+description: |
+  Please provide as much details to help address the issue, including logs and screenshots.
+labels:
+  - bug
+body:
+  - type: checkboxes
+    attributes:
+      label: Checks
+      description: "To ensure timely help, please confirm the following:"
+      options:
+        - label: This template is only for bug reports, usage problems go with 'Help Wanted'.
+          required: true
+        - label: I have thoroughly reviewed the project documentation but couldn't find information to solve my problem.
+          required: true
+        - label: I have searched for existing issues, including closed ones, and couldn't find a solution.
+          required: true
+        - label: I confirm that I am using English to submit this report in order to facilitate communication.
+          required: true
+  - type: textarea
+    attributes:
+      label: Environment Details
+      description: "Provide details such as OS, Python version, and any relevant software or dependencies."
+      placeholder: e.g., CentOS Linux 7, RTX 3090, Python 3.10, torch==2.3.0, cuda 11.8
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Steps to Reproduce
+      description: |
+        Include detailed steps, screenshots, and logs. Use the correct markdown syntax for code blocks.
+      placeholder: |
+        1. Create a new conda environment.
+        2. Clone the repository, install as local editable and properly set up.
+        3. Run the command: `accelerate launch src/f5_tts/train/train.py`.
+        4. Have following error message... (attach logs).
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: ✔️ Expected Behavior
+      placeholder: Describe what you expected to happen.
+    validations:
+      required: false
+  - type: textarea
+    attributes:
+      label: ❌ Actual Behavior
+      placeholder: Describe what actually happened.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,62 @@
+name: "Feature Request"
+description: |
+  Some constructive suggestions and new ideas regarding current repo.
+labels:
+  - enhancement
+body:
+  - type: checkboxes
+    attributes:
+      label: Checks
+      description: "To help us grasp quickly, please confirm the following:"
+      options:
+        - label: This template is only for feature request.
+          required: true
+        - label: I have thoroughly reviewed the project documentation but couldn't find any relevant information that meets my needs.
+          required: true
+        - label: I have searched for existing issues, including closed ones, and found not discussion yet.
+          required: true
+        - label: I confirm that I am using English to submit this report in order to facilitate communication.
+          required: true
+  - type: textarea
+    attributes:
+      label: 1. Is this request related to a challenge you're experiencing? Tell us your story.
+      description: |
+        Describe the specific problem or scenario you're facing in detail. For example:
+        *"I was trying to use [feature] for [specific task], but encountered [issue]. This was frustrating because...."*
+      placeholder: Please describe the situation in as much detail as possible.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: 2. What is your suggested solution?
+      description: |
+        Provide a clear description of the feature or enhancement you'd like to propose. 
+        How would this feature solve your issue or improve the project?
+      placeholder: Describe your idea or proposed solution here.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: 3. Additional context or comments
+      description: |
+        Any other relevant information, links, documents, or screenshots that provide clarity. 
+        Use this section for anything not covered above.
+      placeholder: Add any extra details here.
+    validations:
+      required: false
+
+  - type: checkboxes
+    attributes:
+      label: 4. Can you help us with this feature?
+      description: |
+        Let us know if you're interested in contributing. This is not a commitment but a way to express interest in collaboration.
+      options:
+        - label: I am interested in contributing to this feature.
+          required: false
+
+  - type: markdown
+    attributes:
+      value: |
+        **Note:** Please submit only one request per issue to keep discussions focused and manageable.

.github/ISSUE_TEMPLATE/help_wanted.yml

@@ -0,0 +1,50 @@
+name: "Help Wanted"
+description: |
+  Please provide as much details to help address the issue, including logs and screenshots.
+labels:
+  - help wanted
+body:
+  - type: checkboxes
+    attributes:
+      label: Checks
+      description: "To ensure timely help, please confirm the following:"
+      options:
+        - label: This template is only for usage issues encountered.
+          required: true
+        - label: I have thoroughly reviewed the project documentation but couldn't find information to solve my problem.
+          required: true
+        - label: I have searched for existing issues, including closed ones, and couldn't find a solution.
+          required: true
+        - label: I confirm that I am using English to submit this report in order to facilitate communication.
+          required: true
+  - type: textarea
+    attributes:
+      label: Environment Details
+      description: "Provide details such as OS, Python version, and any relevant software or dependencies."
+      placeholder: e.g., macOS 13.5, Python 3.10, torch==2.3.0, Gradio 4.44.1
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Steps to Reproduce
+      description: |
+        Include detailed steps, screenshots, and logs. Use the correct markdown syntax for code blocks.
+      placeholder: |
+        1. Create a new conda environment.
+        2. Clone the repository and install as pip package.
+        3. Run the command: `f5-tts_infer-gradio` with no ref_text provided.
+        4. Stuck there with the following message... (attach logs and also error msg e.g. after ctrl-c).
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: ✔️ Expected Behavior
+      placeholder: Describe what you expected to happen, e.g. output a generated audio
+    validations:
+      required: false
+  - type: textarea
+    attributes:
+      label: ❌ Actual Behavior
+      placeholder: Describe what actually happened, failure messages, etc.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,26 @@
+name: "Question"
+description: |
+  Pure question or inquiry about the project, usage issue goes with "help wanted".
+labels:
+  - question
+body:
+  - type: checkboxes
+    attributes:
+      label: Checks
+      description: "To help us grasp quickly, please confirm the following:"
+      options:
+        - label: This template is only for question, not feature requests or bug reports.
+          required: true
+        - label: I have thoroughly reviewed the project documentation and read the related paper(s).
+          required: true
+        - label: I have searched for existing issues, including closed ones, no similar questions.
+          required: true
+        - label: I confirm that I am using English to submit this report in order to facilitate communication.
+          required: true
+  - type: textarea
+    attributes:
+      label: Question details
+      description: |
+        Question details, clearly stated using proper markdown syntax.
+    validations:
+      required: true

.github/workflows/pre-commit.yaml

@@ -0,0 +1,14 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v3
+      - uses: pre-commit/[email protected]

.github/workflows/publish-docker-image.yaml

@@ -0,0 +1,60 @@
+name: Create and publish a Docker image
+
+# Configures this workflow to run every time a change is pushed to the branch called `release`.
+on:
+  push:
+    branches: ['main']
+
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
+    permissions:
+      contents: read
+      packages: write
+      # 
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - name: Free Up GitHub Actions Ubuntu Runner Disk Space 🔧
+        uses: jlumbroso/free-disk-space@main
+        with:
+          # This might remove tools that are actually needed, if set to "true" but frees about 6 GB
+          tool-cache: false
+
+          # All of these default to true, but feel free to set to "false" if necessary for your workflow
+          android: true
+          dotnet: true
+          haskell: true
+          large-packages: false
+          swap-storage: false
+          docker-images: false
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - name: Log in to the Container registry
+        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
+      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository.
+      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+      - name: Build and push Docker image
+        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/sync-hf.yaml

@@ -0,0 +1,18 @@
+name: Sync to HF Space
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  trigger_curl:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Send cURL POST request
+      run: |
+        curl -X POST https://mrfakename-sync-f5.hf.space/gradio_api/call/refresh \
+        -s \
+        -H "Content-Type: application/json" \
+        -d "{\"data\": [\"${{ secrets.REFRESH_PASSWORD }}\"]}"

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "src/third_party/BigVGAN"]
+	path = src/third_party/BigVGAN
+	url = https://github.com/NVIDIA/BigVGAN.git

.pre-commit-config.yaml

@@ -0,0 +1,14 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.7.0
+    hooks:
+      # Run the linter.
+      - id: ruff
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+      - id: check-yaml

Dockerfile

@@ -0,0 +1,26 @@
+FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel
+
+USER root
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+LABEL github_repo="https://github.com/SWivid/F5-TTS"
+
+RUN set -x \
+    && apt-get update \
+    && apt-get -y install wget curl man git less openssl libssl-dev unzip unar build-essential aria2 tmux vim \
+    && apt-get install -y openssh-server sox libsox-fmt-all libsox-fmt-mp3 libsndfile1-dev ffmpeg \
+    && apt-get install -y librdmacm1 libibumad3 librdmacm-dev libibverbs1 libibverbs-dev ibverbs-utils ibverbs-providers \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+    
+WORKDIR /workspace
+
+RUN git clone https://github.com/SWivid/F5-TTS.git \
+    && cd F5-TTS \
+    && git submodule update --init --recursive \
+    && pip install -e . --no-cache-dir
+
+ENV SHELL=/bin/bash
+
+WORKDIR /workspace/F5-TTS

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Yushen CHEN
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,13 @@
+---
+title: F5-TTS
+emoji: 🗣️
+colorFrom: green
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: true
+short_description: 'F5-TTS & E2-TTS: Zero-Shot Voice Cloning (Unofficial Demo)'
+sdk_version: 5.9.1
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

README_REPO.md

@@ -0,0 +1,169 @@
+# F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
+
+[![python](https://img.shields.io/badge/Python-3.10-brightgreen)](https://github.com/SWivid/F5-TTS)
+[![arXiv](https://img.shields.io/badge/arXiv-2410.06885-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2410.06885)
+[![demo](https://img.shields.io/badge/GitHub-Demo%20page-orange.svg)](https://swivid.github.io/F5-TTS/)
+[![hfspace](https://img.shields.io/badge/🤗-Space%20demo-yellow)](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
+[![msspace](https://img.shields.io/badge/🤖-Space%20demo-blue)](https://modelscope.cn/studios/modelscope/E2-F5-TTS)
+[![lab](https://img.shields.io/badge/X--LANCE-Lab-grey?labelColor=lightgrey)](https://x-lance.sjtu.edu.cn/)
+<img src="https://github.com/user-attachments/assets/12d7749c-071a-427c-81bf-b87b91def670" alt="Watermark" style="width: 40px; height: auto">
+
+**F5-TTS**: Diffusion Transformer with ConvNeXt V2, faster trained and inference.
+
+**E2 TTS**: Flat-UNet Transformer, closest reproduction from [paper](https://arxiv.org/abs/2406.18009).
+
+**Sway Sampling**: Inference-time flow step sampling strategy, greatly improves performance
+
+### Thanks to all the contributors !
+
+## News
+- **2024/10/08**: F5-TTS & E2 TTS base models on [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), [🟣 Wisemodel](https://wisemodel.cn/models/SJTU_X-LANCE/F5-TTS_Emilia-ZH-EN).
+
+## Installation
+
+```bash
+# Create a python 3.10 conda env (you could also use virtualenv)
+conda create -n f5-tts python=3.10
+conda activate f5-tts
+
+# NVIDIA GPU: install pytorch with your CUDA version, e.g.
+pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
+
+# AMD GPU: install pytorch with your ROCm version, e.g.
+pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
+```
+
+Then you can choose from a few options below:
+
+### 1. As a pip package (if just for inference)
+
+```bash
+pip install git+https://github.com/SWivid/F5-TTS.git
+```
+
+### 2. Local editable (if also do training, finetuning)
+
+```bash
+git clone https://github.com/SWivid/F5-TTS.git
+cd F5-TTS
+# git submodule update --init --recursive  # (optional, if need bigvgan)
+pip install -e .
+```
+
+### 3. Docker usage
+```bash
+# Build from Dockerfile
+docker build -t f5tts:v1 .
+
+# Or pull from GitHub Container Registry
+docker pull ghcr.io/swivid/f5-tts:main
+```
+
+
+## Inference
+
+### 1. Gradio App
+
+Currently supported features:
+
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+- [Custom inference with more language support](src/f5_tts/infer/SHARED.md)
+
+```bash
+# Launch a Gradio app (web interface)
+f5-tts_infer-gradio
+
+# Specify the port/host
+f5-tts_infer-gradio --port 7860 --host 0.0.0.0
+
+# Launch a share link
+f5-tts_infer-gradio --share
+```
+
+### 2. CLI Inference
+
+```bash
+# Run with flags
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model "F5-TTS" \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
+
+# Run with default setting. src/f5_tts/infer/examples/basic/basic.toml
+f5-tts_infer-cli
+# Or with your own .toml file
+f5-tts_infer-cli -c custom.toml
+
+# Multi voice. See src/f5_tts/infer/README.md
+f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
+```
+
+### 3. More instructions
+
+- In order to have better generation results, take a moment to read [detailed guidance](src/f5_tts/infer).
+- The [Issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very useful, please try to find the solution by properly searching the keywords of problem encountered. If no answer found, then feel free to open an issue.
+
+
+## Training
+
+### 1. Gradio App
+
+Read [training & finetuning guidance](src/f5_tts/train) for more instructions.
+
+```bash
+# Quick start with Gradio web interface
+f5-tts_finetune-gradio
+```
+
+
+## [Evaluation](src/f5_tts/eval)
+
+
+## Development
+
+Use pre-commit to ensure code quality (will run linters and formatters automatically)
+
+```bash
+pip install pre-commit
+pre-commit install
+```
+
+When making a pull request, before each commit, run: 
+
+```bash
+pre-commit run --all-files
+```
+
+Note: Some model components have linting exceptions for E722 to accommodate tensor notation
+
+
+## Acknowledgements
+
+- [E2-TTS](https://arxiv.org/abs/2406.18009) brilliant work, simple and effective
+- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763), [LibriTTS](https://arxiv.org/abs/1904.02882), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) valuable datasets
+- [lucidrains](https://github.com/lucidrains) initial CFM structure with also [bfs18](https://github.com/bfs18) for discussion
+- [SD3](https://arxiv.org/abs/2403.03206) & [Hugging Face diffusers](https://github.com/huggingface/diffusers) DiT and MMDiT code structure
+- [torchdiffeq](https://github.com/rtqichen/torchdiffeq) as ODE solver, [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) and [BigVGAN](https://github.com/NVIDIA/BigVGAN) as vocoder
+- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech), [SpeechMOS](https://github.com/tarepan/SpeechMOS) for evaluation tools
+- [ctc-forced-aligner](https://github.com/MahmoudAshraf97/ctc-forced-aligner) for speech edit test
+- [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
+- [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)
+- [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ)
+
+## Citation
+If our work and codebase is useful for you, please cite as:
+```
+@article{chen-etal-2024-f5tts,
+      title={F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching}, 
+      author={Yushen Chen and Zhikang Niu and Ziyang Ma and Keqi Deng and Chunhui Wang and Jian Zhao and Kai Yu and Xie Chen},
+      journal={arXiv preprint arXiv:2410.06885},
+      year={2024},
+}
+```
+## License
+
+Our code is released under MIT License. The pre-trained models are licensed under the CC-BY-NC license due to the training data Emilia, which is an in-the-wild dataset. Sorry for any inconvenience this may cause.

api.py

@@ -0,0 +1,132 @@
+import soundfile as sf
+import torch
+import tqdm
+from cached_path import cached_path
+
+from model import DiT, UNetT
+from model.utils import save_spectrogram
+
+from model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav
+from model.utils import seed_everything
+import random
+import sys
+
+
+class F5TTS:
+    def __init__(
+        self,
+        model_type="F5-TTS",
+        ckpt_file="",
+        vocab_file="",
+        ode_method="euler",
+        use_ema=True,
+        local_path=None,
+        device=None,
+    ):
+        # Initialize parameters
+        self.final_wave = None
+        self.target_sample_rate = 24000
+        self.n_mel_channels = 100
+        self.hop_length = 256
+        self.target_rms = 0.1
+        self.seed = -1
+
+        # Set device
+        self.device = device or (
+            "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+        )
+
+        # Load models
+        self.load_vocoder_model(local_path)
+        self.load_ema_model(model_type, ckpt_file, vocab_file, ode_method, use_ema)
+
+    def load_vocoder_model(self, local_path):
+        self.vocos = load_vocoder(local_path is not None, local_path, self.device)
+
+    def load_ema_model(self, model_type, ckpt_file, vocab_file, ode_method, use_ema):
+        if model_type == "F5-TTS":
+            if not ckpt_file:
+                ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))
+            model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+            model_cls = DiT
+        elif model_type == "E2-TTS":
+            if not ckpt_file:
+                ckpt_file = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
+            model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+            model_cls = UNetT
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+
+        self.ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file, ode_method, use_ema, self.device)
+
+    def export_wav(self, wav, file_wave, remove_silence=False):
+        sf.write(file_wave, wav, self.target_sample_rate)
+
+        if remove_silence:
+            remove_silence_for_generated_wav(file_wave)
+
+    def export_spectrogram(self, spect, file_spect):
+        save_spectrogram(spect, file_spect)
+
+    def infer(
+        self,
+        ref_file,
+        ref_text,
+        gen_text,
+        show_info=print,
+        progress=tqdm,
+        target_rms=0.1,
+        cross_fade_duration=0.15,
+        sway_sampling_coef=-1,
+        cfg_strength=2,
+        nfe_step=32,
+        speed=1.0,
+        fix_duration=None,
+        remove_silence=False,
+        file_wave=None,
+        file_spect=None,
+        seed=-1,
+    ):
+        if seed == -1:
+            seed = random.randint(0, sys.maxsize)
+        seed_everything(seed)
+        self.seed = seed
+        wav, sr, spect = infer_process(
+            ref_file,
+            ref_text,
+            gen_text,
+            self.ema_model,
+            show_info=show_info,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=self.device,
+        )
+
+        if file_wave is not None:
+            self.export_wav(wav, file_wave, remove_silence)
+
+        if file_spect is not None:
+            self.export_spectrogram(spect, file_spect)
+
+        return wav, sr, spect
+
+
+if __name__ == "__main__":
+    f5tts = F5TTS()
+
+    wav, sr, spect = f5tts.infer(
+        ref_file="tests/ref_audio/test_en_1_ref_short.wav",
+        ref_text="some call me nature, others call me mother nature.",
+        gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+        file_wave="tests/out.wav",
+        file_spect="tests/out.png",
+        seed=-1,  # random seed = -1
+    )
+
+    print("seed :", f5tts.seed)

app.py

@@ -0,0 +1,886 @@
+# ruff: noqa: E402
+# Above allows ruff to ignore E402: module level import not at top of file
+
+import json
+import re
+import tempfile
+from collections import OrderedDict
+from importlib.resources import files
+
+import click
+import gradio as gr
+import numpy as np
+import soundfile as sf
+import torchaudio
+from cached_path import cached_path
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+try:
+    import spaces
+
+    USING_SPACES = True
+except ImportError:
+    USING_SPACES = False
+
+
+def gpu_decorator(func):
+    if USING_SPACES:
+        return spaces.GPU(func)
+    else:
+        return func
+
+
+from f5_tts.model import DiT, UNetT
+from f5_tts.infer.utils_infer import (
+    load_vocoder,
+    load_model,
+    preprocess_ref_audio_text,
+    infer_process,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+)
+
+
+DEFAULT_TTS_MODEL = "F5-TTS"
+tts_model_choice = DEFAULT_TTS_MODEL
+
+DEFAULT_TTS_MODEL_CFG = [
+    "hf://Karayakar/F5-TTS-Turkish/model_1200000.safetensors",
+    "hf://Karayakar/F5-TTS-Turkish/vocab.txt",
+    json.dumps(dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)),
+]
+
+
+# load models
+
+vocoder = load_vocoder()
+
+
+def load_f5tts(ckpt_path=str(cached_path("hf://Karayakar/F5-TTS-Turkish/model_1200000.safetensors"))):
+    F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+    return load_model(DiT, F5TTS_model_cfg, ckpt_path)
+
+
+def load_e2tts(ckpt_path=str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))):
+    E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+    return load_model(UNetT, E2TTS_model_cfg, ckpt_path)
+
+
+def load_custom(ckpt_path: str, vocab_path="", model_cfg=None):
+    ckpt_path, vocab_path = ckpt_path.strip(), vocab_path.strip()
+    if ckpt_path.startswith("hf://"):
+        ckpt_path = str(cached_path(ckpt_path))
+    if vocab_path.startswith("hf://"):
+        vocab_path = str(cached_path(vocab_path))
+    if model_cfg is None:
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+    return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path)
+
+
+F5TTS_ema_model = load_f5tts()
+E2TTS_ema_model = load_e2tts() if USING_SPACES else None
+custom_ema_model, pre_custom_path = None, ""
+
+chat_model_state = None
+chat_tokenizer_state = None
+
+
+@gpu_decorator
+def generate_response(messages, model, tokenizer):
+    """Generate response using Qwen"""
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+    )
+
+    generated_ids = [
+        output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
+@gpu_decorator
+def infer(
+    ref_audio_orig,
+    ref_text,
+    gen_text,
+    model,
+    remove_silence,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    speed=1,
+    show_info=gr.Info,
+):
+    if not ref_audio_orig:
+        gr.Warning("Please provide reference audio.")
+        return gr.update(), gr.update(), ref_text
+
+    if not gen_text.strip():
+        gr.Warning("Please enter text to generate.")
+        return gr.update(), gr.update(), ref_text
+
+    ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=show_info)
+
+    if model == "F5-TTS":
+        ema_model = F5TTS_ema_model
+    elif model == "E2-TTS":
+        global E2TTS_ema_model
+        if E2TTS_ema_model is None:
+            show_info("Loading E2-TTS model...")
+            E2TTS_ema_model = load_e2tts()
+        ema_model = E2TTS_ema_model
+    elif isinstance(model, list) and model[0] == "Custom":
+        assert not USING_SPACES, "Only official checkpoints allowed in Spaces."
+        global custom_ema_model, pre_custom_path
+        if pre_custom_path != model[1]:
+            show_info("Loading Custom TTS model...")
+            custom_ema_model = load_custom(model[1], vocab_path=model[2], model_cfg=model[3])
+            pre_custom_path = model[1]
+        ema_model = custom_ema_model
+
+    final_wave, final_sample_rate, combined_spectrogram = infer_process(
+        ref_audio,
+        ref_text,
+        gen_text,
+        ema_model,
+        vocoder,
+        cross_fade_duration=cross_fade_duration,
+        nfe_step=nfe_step,
+        speed=speed,
+        show_info=show_info,
+        progress=gr.Progress(),
+    )
+
+    # Remove silence
+    if remove_silence:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            remove_silence_for_generated_wav(f.name)
+            final_wave, _ = torchaudio.load(f.name)
+        final_wave = final_wave.squeeze().cpu().numpy()
+
+    # Save the spectrogram
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
+        spectrogram_path = tmp_spectrogram.name
+        save_spectrogram(combined_spectrogram, spectrogram_path)
+
+    return (final_sample_rate, final_wave), spectrogram_path, ref_text
+
+
+with gr.Blocks() as app_credits:
+    gr.Markdown("""
+# Credits
+
+
+""")
+with gr.Blocks() as app_tts:
+    gr.Markdown("# Batched TTS")
+    ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
+    gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
+    generate_btn = gr.Button("Synthesize", variant="primary")
+    with gr.Accordion("Advanced Settings", open=False):
+        ref_text_input = gr.Textbox(
+            label="Reference Text",
+            info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.",
+            lines=2,
+        )
+        remove_silence = gr.Checkbox(
+            label="Remove Silences",
+            info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.",
+            value=False,
+        )
+        speed_slider = gr.Slider(
+            label="Speed",
+            minimum=0.3,
+            maximum=2.0,
+            value=1.0,
+            step=0.1,
+            info="Adjust the speed of the audio.",
+        )
+        nfe_slider = gr.Slider(
+            label="NFE Steps",
+            minimum=4,
+            maximum=64,
+            value=32,
+            step=2,
+            info="Set the number of denoising steps.",
+        )
+        cross_fade_duration_slider = gr.Slider(
+            label="Cross-Fade Duration (s)",
+            minimum=0.0,
+            maximum=1.0,
+            value=0.15,
+            step=0.01,
+            info="Set the duration of the cross-fade between audio clips.",
+        )
+
+    audio_output = gr.Audio(label="Synthesized Audio")
+    spectrogram_output = gr.Image(label="Spectrogram")
+
+    @gpu_decorator
+    def basic_tts(
+        ref_audio_input,
+        ref_text_input,
+        gen_text_input,
+        remove_silence,
+        cross_fade_duration_slider,
+        nfe_slider,
+        speed_slider,
+    ):
+        audio_out, spectrogram_path, ref_text_out = infer(
+            ref_audio_input,
+            ref_text_input,
+            gen_text_input,
+            tts_model_choice,
+            remove_silence,
+            cross_fade_duration=cross_fade_duration_slider,
+            nfe_step=nfe_slider,
+            speed=speed_slider,
+        )
+        return audio_out, spectrogram_path, ref_text_out
+
+    generate_btn.click(
+        basic_tts,
+        inputs=[
+            ref_audio_input,
+            ref_text_input,
+            gen_text_input,
+            remove_silence,
+            cross_fade_duration_slider,
+            nfe_slider,
+            speed_slider,
+        ],
+        outputs=[audio_output, spectrogram_output, ref_text_input],
+    )
+
+
+def parse_speechtypes_text(gen_text):
+    # Pattern to find {speechtype}
+    pattern = r"\{(.*?)\}"
+
+    # Split the text by the pattern
+    tokens = re.split(pattern, gen_text)
+
+    segments = []
+
+    current_style = "Regular"
+
+    for i in range(len(tokens)):
+        if i % 2 == 0:
+            # This is text
+            text = tokens[i].strip()
+            if text:
+                segments.append({"style": current_style, "text": text})
+        else:
+            # This is style
+            style = tokens[i].strip()
+            current_style = style
+
+    return segments
+
+
+with gr.Blocks() as app_multistyle:
+    # New section for multistyle generation
+    gr.Markdown(
+        """
+    # Multiple Speech-Type Generation
+
+    This section allows you to generate multiple speech types or multiple people's voices. Enter your text in the format shown below, and the system will generate speech using the appropriate type. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified.
+    """
+    )
+
+    with gr.Row():
+        gr.Markdown(
+            """
+            **Example Input:**                                                                      
+            {Regular} Hello, I'd like to order a sandwich please.                                                         
+            {Surprised} What do you mean you're out of bread?                                                                      
+            {Sad} I really wanted a sandwich though...                                                              
+            {Angry} You know what, darn you and your little shop!                                                                       
+            {Whisper} I'll just go back home and cry now.                                                                           
+            {Shouting} Why me?!                                                                         
+            """
+        )
+
+        gr.Markdown(
+            """
+            **Example Input 2:**                                                                                
+            {Speaker1_Happy} Hello, I'd like to order a sandwich please.                                                            
+            {Speaker2_Regular} Sorry, we're out of bread.                                                                                
+            {Speaker1_Sad} I really wanted a sandwich though...                                                                             
+            {Speaker2_Whisper} I'll give you the last one I was hiding.                                                                     
+            """
+        )
+
+    gr.Markdown(
+        "Upload different audio clips for each speech type. The first speech type is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button."
+    )
+
+    # Regular speech type (mandatory)
+    with gr.Row() as regular_row:
+        with gr.Column():
+            regular_name = gr.Textbox(value="Regular", label="Speech Type Name")
+            regular_insert = gr.Button("Insert Label", variant="secondary")
+        regular_audio = gr.Audio(label="Regular Reference Audio", type="filepath")
+        regular_ref_text = gr.Textbox(label="Reference Text (Regular)", lines=2)
+
+    # Regular speech type (max 100)
+    max_speech_types = 100
+    speech_type_rows = [regular_row]
+    speech_type_names = [regular_name]
+    speech_type_audios = [regular_audio]
+    speech_type_ref_texts = [regular_ref_text]
+    speech_type_delete_btns = [None]
+    speech_type_insert_btns = [regular_insert]
+
+    # Additional speech types (99 more)
+    for i in range(max_speech_types - 1):
+        with gr.Row(visible=False) as row:
+            with gr.Column():
+                name_input = gr.Textbox(label="Speech Type Name")
+                delete_btn = gr.Button("Delete Type", variant="secondary")
+                insert_btn = gr.Button("Insert Label", variant="secondary")
+            audio_input = gr.Audio(label="Reference Audio", type="filepath")
+            ref_text_input = gr.Textbox(label="Reference Text", lines=2)
+        speech_type_rows.append(row)
+        speech_type_names.append(name_input)
+        speech_type_audios.append(audio_input)
+        speech_type_ref_texts.append(ref_text_input)
+        speech_type_delete_btns.append(delete_btn)
+        speech_type_insert_btns.append(insert_btn)
+
+    # Button to add speech type
+    add_speech_type_btn = gr.Button("Add Speech Type")
+
+    # Keep track of autoincrement of speech types, no roll back
+    speech_type_count = 1
+
+    # Function to add a speech type
+    def add_speech_type_fn():
+        row_updates = [gr.update() for _ in range(max_speech_types)]
+        global speech_type_count
+        if speech_type_count < max_speech_types:
+            row_updates[speech_type_count] = gr.update(visible=True)
+            speech_type_count += 1
+        else:
+            gr.Warning("Exhausted maximum number of speech types. Consider restart the app.")
+        return row_updates
+
+    add_speech_type_btn.click(add_speech_type_fn, outputs=speech_type_rows)
+
+    # Function to delete a speech type
+    def delete_speech_type_fn():
+        return gr.update(visible=False), None, None, None
+
+    # Update delete button clicks
+    for i in range(1, len(speech_type_delete_btns)):
+        speech_type_delete_btns[i].click(
+            delete_speech_type_fn,
+            outputs=[speech_type_rows[i], speech_type_names[i], speech_type_audios[i], speech_type_ref_texts[i]],
+        )
+
+    # Text input for the prompt
+    gen_text_input_multistyle = gr.Textbox(
+        label="Text to Generate",
+        lines=10,
+        placeholder="Enter the script with speaker names (or emotion types) at the start of each block, e.g.:\n\n{Regular} Hello, I'd like to order a sandwich please.\n{Surprised} What do you mean you're out of bread?\n{Sad} I really wanted a sandwich though...\n{Angry} You know what, darn you and your little shop!\n{Whisper} I'll just go back home and cry now.\n{Shouting} Why me?!",
+    )
+
+    def make_insert_speech_type_fn(index):
+        def insert_speech_type_fn(current_text, speech_type_name):
+            current_text = current_text or ""
+            speech_type_name = speech_type_name or "None"
+            updated_text = current_text + f"{{{speech_type_name}}} "
+            return updated_text
+
+        return insert_speech_type_fn
+
+    for i, insert_btn in enumerate(speech_type_insert_btns):
+        insert_fn = make_insert_speech_type_fn(i)
+        insert_btn.click(
+            insert_fn,
+            inputs=[gen_text_input_multistyle, speech_type_names[i]],
+            outputs=gen_text_input_multistyle,
+        )
+
+    with gr.Accordion("Advanced Settings", open=False):
+        remove_silence_multistyle = gr.Checkbox(
+            label="Remove Silences",
+            value=True,
+        )
+
+    # Generate button
+    generate_multistyle_btn = gr.Button("Generate Multi-Style Speech", variant="primary")
+
+    # Output audio
+    audio_output_multistyle = gr.Audio(label="Synthesized Audio")
+
+    @gpu_decorator
+    def generate_multistyle_speech(
+        gen_text,
+        *args,
+    ):
+        speech_type_names_list = args[:max_speech_types]
+        speech_type_audios_list = args[max_speech_types : 2 * max_speech_types]
+        speech_type_ref_texts_list = args[2 * max_speech_types : 3 * max_speech_types]
+        remove_silence = args[3 * max_speech_types]
+        # Collect the speech types and their audios into a dict
+        speech_types = OrderedDict()
+
+        ref_text_idx = 0
+        for name_input, audio_input, ref_text_input in zip(
+            speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list
+        ):
+            if name_input and audio_input:
+                speech_types[name_input] = {"audio": audio_input, "ref_text": ref_text_input}
+            else:
+                speech_types[f"@{ref_text_idx}@"] = {"audio": "", "ref_text": ""}
+            ref_text_idx += 1
+
+        # Parse the gen_text into segments
+        segments = parse_speechtypes_text(gen_text)
+
+        # For each segment, generate speech
+        generated_audio_segments = []
+        current_style = "Regular"
+
+        for segment in segments:
+            style = segment["style"]
+            text = segment["text"]
+
+            if style in speech_types:
+                current_style = style
+            else:
+                gr.Warning(f"Type {style} is not available, will use Regular as default.")
+                current_style = "Regular"
+
+            try:
+                ref_audio = speech_types[current_style]["audio"]
+            except KeyError:
+                gr.Warning(f"Please provide reference audio for type {current_style}.")
+                return [None] + [speech_types[style]["ref_text"] for style in speech_types]
+            ref_text = speech_types[current_style].get("ref_text", "")
+
+            # Generate speech for this segment
+            audio_out, _, ref_text_out = infer(
+                ref_audio, ref_text, text, tts_model_choice, remove_silence, 0, show_info=print
+            )  # show_info=print no pull to top when generating
+            sr, audio_data = audio_out
+
+            generated_audio_segments.append(audio_data)
+            speech_types[current_style]["ref_text"] = ref_text_out
+
+        # Concatenate all audio segments
+        if generated_audio_segments:
+            final_audio_data = np.concatenate(generated_audio_segments)
+            return [(sr, final_audio_data)] + [speech_types[style]["ref_text"] for style in speech_types]
+        else:
+            gr.Warning("No audio generated.")
+            return [None] + [speech_types[style]["ref_text"] for style in speech_types]
+
+    generate_multistyle_btn.click(
+        generate_multistyle_speech,
+        inputs=[
+            gen_text_input_multistyle,
+        ]
+        + speech_type_names
+        + speech_type_audios
+        + speech_type_ref_texts
+        + [
+            remove_silence_multistyle,
+        ],
+        outputs=[audio_output_multistyle] + speech_type_ref_texts,
+    )
+
+    # Validation function to disable Generate button if speech types are missing
+    def validate_speech_types(gen_text, regular_name, *args):
+        speech_type_names_list = args
+
+        # Collect the speech types names
+        speech_types_available = set()
+        if regular_name:
+            speech_types_available.add(regular_name)
+        for name_input in speech_type_names_list:
+            if name_input:
+                speech_types_available.add(name_input)
+
+        # Parse the gen_text to get the speech types used
+        segments = parse_speechtypes_text(gen_text)
+        speech_types_in_text = set(segment["style"] for segment in segments)
+
+        # Check if all speech types in text are available
+        missing_speech_types = speech_types_in_text - speech_types_available
+
+        if missing_speech_types:
+            # Disable the generate button
+            return gr.update(interactive=False)
+        else:
+            # Enable the generate button
+            return gr.update(interactive=True)
+
+    gen_text_input_multistyle.change(
+        validate_speech_types,
+        inputs=[gen_text_input_multistyle, regular_name] + speech_type_names,
+        outputs=generate_multistyle_btn,
+    )
+
+
+with gr.Blocks() as app_chat:
+    gr.Markdown(
+        """
+# Voice Chat
+Have a conversation with an AI using your reference voice! 
+1. Upload a reference audio clip and optionally its transcript.
+2. Load the chat model.
+3. Record your message through your microphone.
+4. The AI will respond using the reference voice.
+"""
+    )
+
+    if not USING_SPACES:
+        load_chat_model_btn = gr.Button("Load Chat Model", variant="primary")
+
+        chat_interface_container = gr.Column(visible=False)
+
+        @gpu_decorator
+        def load_chat_model():
+            global chat_model_state, chat_tokenizer_state
+            if chat_model_state is None:
+                show_info = gr.Info
+                show_info("Loading chat model...")
+                model_name = "Qwen/Qwen2.5-3B-Instruct"
+                chat_model_state = AutoModelForCausalLM.from_pretrained(
+                    model_name, torch_dtype="auto", device_map="auto"
+                )
+                chat_tokenizer_state = AutoTokenizer.from_pretrained(model_name)
+                show_info("Chat model loaded.")
+
+            return gr.update(visible=False), gr.update(visible=True)
+
+        load_chat_model_btn.click(load_chat_model, outputs=[load_chat_model_btn, chat_interface_container])
+
+    else:
+        chat_interface_container = gr.Column()
+
+        if chat_model_state is None:
+            model_name = "Qwen/Qwen2.5-3B-Instruct"
+            chat_model_state = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+            chat_tokenizer_state = AutoTokenizer.from_pretrained(model_name)
+
+    with chat_interface_container:
+        with gr.Row():
+            with gr.Column():
+                ref_audio_chat = gr.Audio(label="Reference Audio", type="filepath")
+            with gr.Column():
+                with gr.Accordion("Advanced Settings", open=False):
+                    remove_silence_chat = gr.Checkbox(
+                        label="Remove Silences",
+                        value=True,
+                    )
+                    ref_text_chat = gr.Textbox(
+                        label="Reference Text",
+                        info="Optional: Leave blank to auto-transcribe",
+                        lines=2,
+                    )
+                    system_prompt_chat = gr.Textbox(
+                        label="System Prompt",
+                        value="You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                        lines=2,
+                    )
+
+        chatbot_interface = gr.Chatbot(label="Conversation")
+
+        with gr.Row():
+            with gr.Column():
+                audio_input_chat = gr.Microphone(
+                    label="Speak your message",
+                    type="filepath",
+                )
+                audio_output_chat = gr.Audio(autoplay=True)
+            with gr.Column():
+                text_input_chat = gr.Textbox(
+                    label="Type your message",
+                    lines=1,
+                )
+                send_btn_chat = gr.Button("Send Message")
+                clear_btn_chat = gr.Button("Clear Conversation")
+
+        conversation_state = gr.State(
+            value=[
+                {
+                    "role": "system",
+                    "content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                }
+            ]
+        )
+
+        # Modify process_audio_input to use model and tokenizer from state
+        @gpu_decorator
+        def process_audio_input(audio_path, text, history, conv_state):
+            """Handle audio or text input from user"""
+
+            if not audio_path and not text.strip():
+                return history, conv_state, ""
+
+            if audio_path:
+                text = preprocess_ref_audio_text(audio_path, text)[1]
+
+            if not text.strip():
+                return history, conv_state, ""
+
+            conv_state.append({"role": "user", "content": text})
+            history.append((text, None))
+
+            response = generate_response(conv_state, chat_model_state, chat_tokenizer_state)
+
+            conv_state.append({"role": "assistant", "content": response})
+            history[-1] = (text, response)
+
+            return history, conv_state, ""
+
+        @gpu_decorator
+        def generate_audio_response(history, ref_audio, ref_text, remove_silence):
+            """Generate TTS audio for AI response"""
+            if not history or not ref_audio:
+                return None
+
+            last_user_message, last_ai_response = history[-1]
+            if not last_ai_response:
+                return None
+
+            audio_result, _, ref_text_out = infer(
+                ref_audio,
+                ref_text,
+                last_ai_response,
+                tts_model_choice,
+                remove_silence,
+                cross_fade_duration=0.15,
+                speed=1.0,
+                show_info=print,  # show_info=print no pull to top when generating
+            )
+            return audio_result, ref_text_out
+
+        def clear_conversation():
+            """Reset the conversation"""
+            return [], [
+                {
+                    "role": "system",
+                    "content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                }
+            ]
+
+        def update_system_prompt(new_prompt):
+            """Update the system prompt and reset the conversation"""
+            new_conv_state = [{"role": "system", "content": new_prompt}]
+            return [], new_conv_state
+
+        # Handle audio input
+        audio_input_chat.stop_recording(
+            process_audio_input,
+            inputs=[audio_input_chat, text_input_chat, chatbot_interface, conversation_state],
+            outputs=[chatbot_interface, conversation_state],
+        ).then(
+            generate_audio_response,
+            inputs=[chatbot_interface, ref_audio_chat, ref_text_chat, remove_silence_chat],
+            outputs=[audio_output_chat, ref_text_chat],
+        ).then(
+            lambda: None,
+            None,
+            audio_input_chat,
+        )
+
+        # Handle text input
+        text_input_chat.submit(
+            process_audio_input,
+            inputs=[audio_input_chat, text_input_chat, chatbot_interface, conversation_state],
+            outputs=[chatbot_interface, conversation_state],
+        ).then(
+            generate_audio_response,
+            inputs=[chatbot_interface, ref_audio_chat, ref_text_chat, remove_silence_chat],
+            outputs=[audio_output_chat, ref_text_chat],
+        ).then(
+            lambda: None,
+            None,
+            text_input_chat,
+        )
+
+        # Handle send button
+        send_btn_chat.click(
+            process_audio_input,
+            inputs=[audio_input_chat, text_input_chat, chatbot_interface, conversation_state],
+            outputs=[chatbot_interface, conversation_state],
+        ).then(
+            generate_audio_response,
+            inputs=[chatbot_interface, ref_audio_chat, ref_text_chat, remove_silence_chat],
+            outputs=[audio_output_chat, ref_text_chat],
+        ).then(
+            lambda: None,
+            None,
+            text_input_chat,
+        )
+
+        # Handle clear button
+        clear_btn_chat.click(
+            clear_conversation,
+            outputs=[chatbot_interface, conversation_state],
+        )
+
+        # Handle system prompt change and reset conversation
+        system_prompt_chat.change(
+            update_system_prompt,
+            inputs=system_prompt_chat,
+            outputs=[chatbot_interface, conversation_state],
+        )
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        f"""
+# E2/F5 TTS
+
+This is {"a local web UI for [F5 TTS](https://github.com/SWivid/F5-TTS)" if not USING_SPACES else "an online demo for [F5-TTS](https://github.com/SWivid/F5-TTS)"} with advanced batch processing support. This app supports the following TTS models:
+
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
+
+The checkpoints currently support English and Chinese.
+
+If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s with  ✂  in the bottom right corner (otherwise might have non-optimal auto-trimmed result).
+
+**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
+"""
+    )
+
+    last_used_custom = files("f5_tts").joinpath("infer/.cache/last_used_custom_model_info.txt")
+
+    def load_last_used_custom():
+        try:
+            custom = []
+            with open(last_used_custom, "r", encoding="utf-8") as f:
+                for line in f:
+                    custom.append(line.strip())
+            return custom
+        except FileNotFoundError:
+            last_used_custom.parent.mkdir(parents=True, exist_ok=True)
+            return DEFAULT_TTS_MODEL_CFG
+
+    def switch_tts_model(new_choice):
+        global tts_model_choice
+        if new_choice == "Custom":  # override in case webpage is refreshed
+            custom_ckpt_path, custom_vocab_path, custom_model_cfg = load_last_used_custom()
+            tts_model_choice = ["Custom", custom_ckpt_path, custom_vocab_path, json.loads(custom_model_cfg)]
+            return (
+                gr.update(visible=True, value=custom_ckpt_path),
+                gr.update(visible=True, value=custom_vocab_path),
+                gr.update(visible=True, value=custom_model_cfg),
+            )
+        else:
+            tts_model_choice = new_choice
+            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+
+    def set_custom_model(custom_ckpt_path, custom_vocab_path, custom_model_cfg):
+        global tts_model_choice
+        tts_model_choice = ["Custom", custom_ckpt_path, custom_vocab_path, json.loads(custom_model_cfg)]
+        with open(last_used_custom, "w", encoding="utf-8") as f:
+            f.write(custom_ckpt_path + "\n" + custom_vocab_path + "\n" + custom_model_cfg + "\n")
+
+    with gr.Row():
+        if not USING_SPACES:
+            choose_tts_model = gr.Radio(
+                choices=[DEFAULT_TTS_MODEL, "E2-TTS", "Custom"], label="Choose TTS Model", value=DEFAULT_TTS_MODEL
+            )
+        else:
+            choose_tts_model = gr.Radio(
+                choices=[DEFAULT_TTS_MODEL, "E2-TTS"], label="Choose TTS Model", value=DEFAULT_TTS_MODEL
+            )
+        custom_ckpt_path = gr.Dropdown(
+            choices=[DEFAULT_TTS_MODEL_CFG[0]],
+            value=load_last_used_custom()[0],
+            allow_custom_value=True,
+            label="Model: local_path | hf://user_id/repo_id/model_ckpt",
+            visible=False,
+        )
+        custom_vocab_path = gr.Dropdown(
+            choices=[DEFAULT_TTS_MODEL_CFG[1]],
+            value=load_last_used_custom()[1],
+            allow_custom_value=True,
+            label="Vocab: local_path | hf://user_id/repo_id/vocab_file",
+            visible=False,
+        )
+        custom_model_cfg = gr.Dropdown(
+            choices=[
+                DEFAULT_TTS_MODEL_CFG[2],
+                json.dumps(dict(dim=768, depth=18, heads=12, ff_mult=2, text_dim=512, conv_layers=4)),
+            ],
+            value=load_last_used_custom()[2],
+            allow_custom_value=True,
+            label="Config: in a dictionary form",
+            visible=False,
+        )
+
+    choose_tts_model.change(
+        switch_tts_model,
+        inputs=[choose_tts_model],
+        outputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_ckpt_path.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_vocab_path.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+    custom_model_cfg.change(
+        set_custom_model,
+        inputs=[custom_ckpt_path, custom_vocab_path, custom_model_cfg],
+        show_progress="hidden",
+    )
+
+    gr.TabbedInterface(
+        [app_tts, app_multistyle, app_chat, app_credits],
+        ["Basic-TTS", "Multi-Speech", "Voice-Chat", "Credits"],
+    )
+
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+@click.option(
+    "--root_path",
+    "-r",
+    default=None,
+    type=str,
+    help='The root path (or "mount point") of the application, if it\'s not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application, e.g. set "/myapp" or full URL for application served at "https://example.com/myapp".',
+)
+def main(port, host, share, api, root_path):
+    global app
+    print("Starting app...")
+    app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api, root_path=root_path)
+
+
+if __name__ == "__main__":
+    if not USING_SPACES:
+        main()
+    else:
+        app.queue().launch()

ckpts/README.md

@@ -0,0 +1,10 @@
+
+Pretrained model ckpts. https://huggingface.co/SWivid/F5-TTS
+
+```
+ckpts/
+    E2TTS_Base/
+        model_1200000.pt
+    F5TTS_Base/
+        model_1200000.pt
+```

data/Emilia_ZH_EN_pinyin/vocab.txt

@@ -0,0 +1,2545 @@
+ 
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
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+7
+8
+9
+:
+;
+=
+>
+?
+@
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+C
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+[
+\
+]
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+뜻
+띠
+라
+락
+란
+람
+랍
+랑
+래
+랜
+러
+런
+럼
+렇
+레
+려
+력
+렵
+렸
+로
+록
+롬
+루
+르
+른
+를
+름
+릉
+리
+릴
+림
+마
+막
+만
+많
+말
+맑
+맙
+맛
+매
+머
+먹
+멍
+메
+면
+명
+몇
+모
+목
+몸
+못
+무
+문
+물
+뭐
+뭘
+미
+민
+밌
+밑
+바
+박
+밖
+반
+받
+발
+밤
+밥
+방
+배
+백
+밸
+뱀
+버
+번
+벌
+벚
+베
+벼
+벽
+별
+병
+보
+복
+본
+볼
+봐
+봤
+부
+분
+불
+비
+빔
+빛
+빠
+빨
+뼈
+뽀
+뿅
+쁘
+사
+산
+살
+삼
+샀
+상
+새
+색
+생
+서
+선
+설
+섭
+섰
+성
+세
+셔
+션
+셨
+소
+속
+손
+송
+수
+숙
+순
+술
+숫
+숭
+숲
+쉬
+쉽
+스
+슨
+습
+슷
+시
+식
+신
+실
+싫
+심
+십
+싶
+싸
+써
+쓰
+쓴
+씌
+씨
+씩
+씬
+아
+악
+안
+않
+알
+야
+약
+얀
+양
+얘
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+언
+얼
+엄
+업
+없
+었
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+에
+여
+역
+연
+염
+엽
+영
+옆
+예
+옛
+오
+온
+올
+옷
+옹
+와
+왔
+왜
+요
+욕
+용
+우
+운
+울
+웃
+워
+원
+월
+웠
+위
+윙
+유
+육
+윤
+으
+은
+을
+음
+응
+의
+이
+익
+인
+일
+읽
+임
+입
+있
+자
+작
+잔
+잖
+잘
+잡
+잤
+장
+재
+저
+전
+점
+정
+제
+져
+졌
+조
+족
+좀
+종
+좋
+죠
+주
+준
+줄
+중
+줘
+즈
+즐
+즘
+지
+진
+집
+짜
+짝
+쩌
+쪼
+쪽
+쫌
+쭈
+쯔
+찌
+찍
+차
+착
+찾
+책
+처
+천
+철
+체
+쳐
+쳤
+초
+촌
+추
+출
+춤
+춥
+춰
+치
+친
+칠
+침
+칩
+칼
+커
+켓
+코
+콩
+쿠
+퀴
+크
+큰
+큽
+키
+킨
+타
+태
+터
+턴
+털
+테
+토
+통
+투
+트
+특
+튼
+틀
+티
+팀
+파
+팔
+패
+페
+펜
+펭
+평
+포
+폭
+표
+품
+풍
+프
+플
+피
+필
+하
+학
+한
+할
+함
+합
+항
+해
+햇
+했
+행
+허
+험
+형
+혜
+호
+혼
+홀
+화
+회
+획
+후
+휴
+흐
+흔
+희
+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶

finetune-cli.py

@@ -0,0 +1,127 @@
+import argparse
+from model import CFM, UNetT, DiT, Trainer
+from model.utils import get_tokenizer
+from model.dataset import load_dataset
+from cached_path import cached_path
+import shutil
+import os
+
+# -------------------------- Dataset Settings --------------------------- #
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+
+
+# -------------------------- Argument Parsing --------------------------- #
+def parse_args():
+    parser = argparse.ArgumentParser(description="Train CFM Model")
+
+    parser.add_argument(
+        "--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
+    )
+    parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
+    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training")
+    parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU")
+    parser.add_argument(
+        "--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
+    )
+    parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch")
+    parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
+    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
+    parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
+    parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps")
+    parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps")
+    parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps")
+    parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
+
+    parser.add_argument(
+        "--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        type=str,
+        default=None,
+        help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
+    )
+
+    return parser.parse_args()
+
+
+# -------------------------- Training Settings -------------------------- #
+
+
+def main():
+    args = parse_args()
+
+    # Model parameters based on experiment name
+    if args.exp_name == "F5TTS_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        if args.finetune:
+            ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+    elif args.exp_name == "E2TTS_Base":
+        wandb_resume_id = None
+        model_cls = UNetT
+        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+        if args.finetune:
+            ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+
+    if args.finetune:
+        path_ckpt = os.path.join("ckpts", args.dataset_name)
+        if not os.path.isdir(path_ckpt):
+            os.makedirs(path_ckpt, exist_ok=True)
+            shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
+
+    checkpoint_path = os.path.join("ckpts", args.dataset_name)
+
+    # Use the tokenizer and tokenizer_path provided in the command line arguments
+    tokenizer = args.tokenizer
+    if tokenizer == "custom":
+        if not args.tokenizer_path:
+            raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
+        tokenizer_path = args.tokenizer_path
+    else:
+        tokenizer_path = args.dataset_name
+
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    mel_spec_kwargs = dict(
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+    )
+
+    e2tts = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=mel_spec_kwargs,
+        vocab_char_map=vocab_char_map,
+    )
+
+    trainer = Trainer(
+        e2tts,
+        args.epochs,
+        args.learning_rate,
+        num_warmup_updates=args.num_warmup_updates,
+        save_per_updates=args.save_per_updates,
+        checkpoint_path=checkpoint_path,
+        batch_size=args.batch_size_per_gpu,
+        batch_size_type=args.batch_size_type,
+        max_samples=args.max_samples,
+        grad_accumulation_steps=args.grad_accumulation_steps,
+        max_grad_norm=args.max_grad_norm,
+        wandb_project="CFM-TTS",
+        wandb_run_name=args.exp_name,
+        wandb_resume_id=wandb_resume_id,
+        last_per_steps=args.last_per_steps,
+    )
+
+    train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
+    trainer.train(
+        train_dataset,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
+
+
+if __name__ == "__main__":
+    main()

finetune_gradio.py

@@ -0,0 +1,944 @@
+import os
+import sys
+
+import tempfile
+import random
+from transformers import pipeline
+import gradio as gr
+import torch
+import gc
+import click
+import torchaudio
+from glob import glob
+import librosa
+import numpy as np
+from scipy.io import wavfile
+import shutil
+import time
+
+import json
+from model.utils import convert_char_to_pinyin
+import signal
+import psutil
+import platform
+import subprocess
+from datasets.arrow_writer import ArrowWriter
+from datasets import Dataset as Dataset_
+from api import F5TTS
+
+
+training_process = None
+system = platform.system()
+python_executable = sys.executable or "python"
+tts_api = None
+last_checkpoint = ""
+last_device = ""
+
+path_data = "data"
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+pipe = None
+
+
+# Load metadata
+def get_audio_duration(audio_path):
+    """Calculate the duration of an audio file."""
+    audio, sample_rate = torchaudio.load(audio_path)
+    num_channels = audio.shape[0]
+    return audio.shape[1] / (sample_rate * num_channels)
+
+
+def clear_text(text):
+    """Clean and prepare text by lowering the case and stripping whitespace."""
+    return text.lower().strip()
+
+
+def get_rms(
+    y,
+    frame_length=2048,
+    hop_length=512,
+    pad_mode="constant",
+):  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    padding = (int(frame_length // 2), int(frame_length // 2))
+    y = np.pad(y, padding, mode=pad_mode)
+
+    axis = -1
+    # put our new within-frame axis at the end for now
+    out_strides = y.strides + tuple([y.strides[axis]])
+    # Reduce the shape on the framing axis
+    x_shape_trimmed = list(y.shape)
+    x_shape_trimmed[axis] -= frame_length - 1
+    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
+    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
+    if axis < 0:
+        target_axis = axis - 1
+    else:
+        target_axis = axis + 1
+    xw = np.moveaxis(xw, -1, target_axis)
+    # Downsample along the target axis
+    slices = [slice(None)] * xw.ndim
+    slices[axis] = slice(0, None, hop_length)
+    x = xw[tuple(slices)]
+
+    # Calculate power
+    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
+
+    return np.sqrt(power)
+
+
+class Slicer:  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    def __init__(
+        self,
+        sr: int,
+        threshold: float = -40.0,
+        min_length: int = 2000,
+        min_interval: int = 300,
+        hop_size: int = 20,
+        max_sil_kept: int = 2000,
+    ):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
+        if not max_sil_kept >= hop_size:
+            raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.0)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
+        else:
+            return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = waveform.mean(axis=0)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return [waveform]
+        rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if silence_start is not None and total_frames - silence_start >= self.min_interval:
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        ####音频+起始时间+终止时间
+        if len(sil_tags) == 0:
+            return [[waveform, 0, int(total_frames * self.hop_size)]]
+        else:
+            chunks = []
+            if sil_tags[0][0] > 0:
+                chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
+            for i in range(len(sil_tags) - 1):
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
+                        int(sil_tags[i][1] * self.hop_size),
+                        int(sil_tags[i + 1][0] * self.hop_size),
+                    ]
+                )
+            if sil_tags[-1][1] < total_frames:
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[-1][1], total_frames),
+                        int(sil_tags[-1][1] * self.hop_size),
+                        int(total_frames * self.hop_size),
+                    ]
+                )
+            return chunks
+
+
+# terminal
+def terminate_process_tree(pid, including_parent=True):
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+
+def terminate_process(pid):
+    if system == "Windows":
+        cmd = f"taskkill /t /f /pid {pid}"
+        os.system(cmd)
+    else:
+        terminate_process_tree(pid)
+
+
+def start_training(
+    dataset_name="",
+    exp_name="F5TTS_Base",
+    learning_rate=1e-4,
+    batch_size_per_gpu=400,
+    batch_size_type="frame",
+    max_samples=64,
+    grad_accumulation_steps=1,
+    max_grad_norm=1.0,
+    epochs=11,
+    num_warmup_updates=200,
+    save_per_updates=400,
+    last_per_steps=800,
+    finetune=True,
+):
+    global training_process, tts_api
+
+    if tts_api is not None:
+        del tts_api
+        gc.collect()
+        torch.cuda.empty_cache()
+        tts_api = None
+
+    path_project = os.path.join(path_data, dataset_name + "_pinyin")
+
+    if not os.path.isdir(path_project):
+        yield (
+            f"There is not project with name {dataset_name}",
+            gr.update(interactive=True),
+            gr.update(interactive=False),
+        )
+        return
+
+    file_raw = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_raw):
+        yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
+        return
+
+    # Check if a training process is already running
+    if training_process is not None:
+        return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
+
+    yield "start train", gr.update(interactive=False), gr.update(interactive=False)
+
+    # Command to run the training script with the specified arguments
+    cmd = (
+        f"accelerate launch finetune-cli.py --exp_name {exp_name} "
+        f"--learning_rate {learning_rate} "
+        f"--batch_size_per_gpu {batch_size_per_gpu} "
+        f"--batch_size_type {batch_size_type} "
+        f"--max_samples {max_samples} "
+        f"--grad_accumulation_steps {grad_accumulation_steps} "
+        f"--max_grad_norm {max_grad_norm} "
+        f"--epochs {epochs} "
+        f"--num_warmup_updates {num_warmup_updates} "
+        f"--save_per_updates {save_per_updates} "
+        f"--last_per_steps {last_per_steps} "
+        f"--dataset_name {dataset_name}"
+    )
+    if finetune:
+        cmd += f" --finetune {finetune}"
+
+    print(cmd)
+
+    try:
+        # Start the training process
+        training_process = subprocess.Popen(cmd, shell=True)
+
+        time.sleep(5)
+        yield "train start", gr.update(interactive=False), gr.update(interactive=True)
+
+        # Wait for the training process to finish
+        training_process.wait()
+        time.sleep(1)
+
+        if training_process is None:
+            text_info = "train stop"
+        else:
+            text_info = "train complete !"
+
+    except Exception as e:  # Catch all exceptions
+        # Ensure that we reset the training process variable in case of an error
+        text_info = f"An error occurred: {str(e)}"
+
+    training_process = None
+
+    yield text_info, gr.update(interactive=True), gr.update(interactive=False)
+
+
+def stop_training():
+    global training_process
+    if training_process is None:
+        return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
+    terminate_process_tree(training_process.pid)
+    training_process = None
+    return "train stop", gr.update(interactive=True), gr.update(interactive=False)
+
+
+def create_data_project(name):
+    name += "_pinyin"
+    os.makedirs(os.path.join(path_data, name), exist_ok=True)
+    os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
+
+
+def transcribe(file_audio, language="english"):
+    global pipe
+
+    if pipe is None:
+        pipe = pipeline(
+            "automatic-speech-recognition",
+            model="openai/whisper-large-v3-turbo",
+            torch_dtype=torch.float16,
+            device=device,
+        )
+
+    text_transcribe = pipe(
+        file_audio,
+        chunk_length_s=30,
+        batch_size=128,
+        generate_kwargs={"task": "transcribe", "language": language},
+        return_timestamps=False,
+    )["text"].strip()
+    return text_transcribe
+
+
+def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
+    name_project += "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+    path_dataset = os.path.join(path_project, "dataset")
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    if audio_files is None:
+        return "You need to load an audio file."
+
+    if os.path.isdir(path_project_wavs):
+        shutil.rmtree(path_project_wavs)
+
+    if os.path.isfile(file_metadata):
+        os.remove(file_metadata)
+
+    os.makedirs(path_project_wavs, exist_ok=True)
+
+    if user:
+        file_audios = [
+            file
+            for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
+            for file in glob(os.path.join(path_dataset, format))
+        ]
+        if file_audios == []:
+            return "No audio file was found in the dataset."
+    else:
+        file_audios = audio_files
+
+    alpha = 0.5
+    _max = 1.0
+    slicer = Slicer(24000)
+
+    num = 0
+    error_num = 0
+    data = ""
+    for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
+        audio, _ = librosa.load(file_audio, sr=24000, mono=True)
+
+        list_slicer = slicer.slice(audio)
+        for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
+            name_segment = os.path.join(f"segment_{num}")
+            file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
+
+            tmp_max = np.abs(chunk).max()
+            if tmp_max > 1:
+                chunk /= tmp_max
+            chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
+            wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
+
+            try:
+                text = transcribe(file_segment, language)
+                text = text.lower().strip().replace('"', "")
+
+                data += f"{name_segment}|{text}\n"
+
+                num += 1
+            except:  # noqa: E722
+                error_num += 1
+
+    with open(file_metadata, "w", encoding="utf-8") as f:
+        f.write(data)
+
+    if error_num != []:
+        error_text = f"\nerror files : {error_num}"
+    else:
+        error_text = ""
+
+    return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
+
+
+def format_seconds_to_hms(seconds):
+    hours = int(seconds / 3600)
+    minutes = int((seconds % 3600) / 60)
+    seconds = seconds % 60
+    return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
+
+
+def create_metadata(name_project, progress=gr.Progress()):
+    name_project += "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    file_raw = os.path.join(path_project, "raw.arrow")
+    file_duration = os.path.join(path_project, "duration.json")
+    file_vocab = os.path.join(path_project, "vocab.txt")
+
+    if not os.path.isfile(file_metadata):
+        return "The file was not found in " + file_metadata
+
+    with open(file_metadata, "r", encoding="utf-8") as f:
+        data = f.read()
+
+    audio_path_list = []
+    text_list = []
+    duration_list = []
+
+    count = data.split("\n")
+    lenght = 0
+    result = []
+    error_files = []
+    for line in progress.tqdm(data.split("\n"), total=count):
+        sp_line = line.split("|")
+        if len(sp_line) != 2:
+            continue
+        name_audio, text = sp_line[:2]
+
+        file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
+
+        if not os.path.isfile(file_audio):
+            error_files.append(file_audio)
+            continue
+
+        duraction = get_audio_duration(file_audio)
+        if duraction < 2 and duraction > 15:
+            continue
+        if len(text) < 4:
+            continue
+
+        text = clear_text(text)
+        text = convert_char_to_pinyin([text], polyphone=True)[0]
+
+        audio_path_list.append(file_audio)
+        duration_list.append(duraction)
+        text_list.append(text)
+
+        result.append({"audio_path": file_audio, "text": text, "duration": duraction})
+
+        lenght += duraction
+
+    if duration_list == []:
+        error_files_text = "\n".join(error_files)
+        return f"Error: No audio files found in the specified path : \n{error_files_text}"
+
+    min_second = round(min(duration_list), 2)
+    max_second = round(max(duration_list), 2)
+
+    with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
+        for line in progress.tqdm(result, total=len(result), desc="prepare data"):
+            writer.write(line)
+
+    with open(file_duration, "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"
+    if not os.path.isfile(file_vocab_finetune):
+        return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
+    shutil.copy2(file_vocab_finetune, file_vocab)
+
+    if error_files != []:
+        error_text = "error files\n" + "\n".join(error_files)
+    else:
+        error_text = ""
+
+    return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}"
+
+
+def check_user(value):
+    return gr.update(visible=not value), gr.update(visible=value)
+
+
+def calculate_train(
+    name_project,
+    batch_size_type,
+    max_samples,
+    learning_rate,
+    num_warmup_updates,
+    save_per_updates,
+    last_per_steps,
+    finetune,
+):
+    name_project += "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+    file_duraction = os.path.join(path_project, "duration.json")
+
+    if not os.path.isfile(file_duraction):
+        return (
+            1000,
+            max_samples,
+            num_warmup_updates,
+            save_per_updates,
+            last_per_steps,
+            "project not found !",
+            learning_rate,
+        )
+
+    with open(file_duraction, "r") as file:
+        data = json.load(file)
+
+    duration_list = data["duration"]
+
+    samples = len(duration_list)
+
+    if torch.cuda.is_available():
+        gpu_properties = torch.cuda.get_device_properties(0)
+        total_memory = gpu_properties.total_memory / (1024**3)
+    elif torch.backends.mps.is_available():
+        total_memory = psutil.virtual_memory().available / (1024**3)
+
+    if batch_size_type == "frame":
+        batch = int(total_memory * 0.5)
+        batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
+        batch_size_per_gpu = int(38400 / batch)
+    else:
+        batch_size_per_gpu = int(total_memory / 8)
+        batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
+        batch = batch_size_per_gpu
+
+    if batch_size_per_gpu <= 0:
+        batch_size_per_gpu = 1
+
+    if samples < 64:
+        max_samples = int(samples * 0.25)
+    else:
+        max_samples = 64
+
+    num_warmup_updates = int(samples * 0.05)
+    save_per_updates = int(samples * 0.10)
+    last_per_steps = int(save_per_updates * 5)
+
+    max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
+    num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
+    save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
+    last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
+
+    if finetune:
+        learning_rate = 1e-5
+    else:
+        learning_rate = 7.5e-5
+
+    return batch_size_per_gpu, max_samples, num_warmup_updates, save_per_updates, last_per_steps, samples, learning_rate
+
+
+def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -> None:
+    try:
+        checkpoint = torch.load(checkpoint_path)
+        print("Original Checkpoint Keys:", checkpoint.keys())
+
+        ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
+
+        if ema_model_state_dict is not None:
+            new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
+            torch.save(new_checkpoint, new_checkpoint_path)
+            return f"New checkpoint saved at: {new_checkpoint_path}"
+        else:
+            return "No 'ema_model_state_dict' found in the checkpoint."
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+
+def vocab_check(project_name):
+    name_project = project_name + "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt"
+    if not os.path.isfile(file_vocab):
+        return f"the file {file_vocab} not found !"
+
+    with open(file_vocab, "r", encoding="utf-8") as f:
+        data = f.read()
+
+    vocab = data.split("\n")
+
+    if not os.path.isfile(file_metadata):
+        return f"the file {file_metadata} not found !"
+
+    with open(file_metadata, "r", encoding="utf-8") as f:
+        data = f.read()
+
+    miss_symbols = []
+    miss_symbols_keep = {}
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+
+        text = sp[1].lower().strip()
+
+        for t in text:
+            if t not in vocab and t not in miss_symbols_keep:
+                miss_symbols.append(t)
+                miss_symbols_keep[t] = t
+    if miss_symbols == []:
+        info = "You can train using your language !"
+    else:
+        info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)
+
+    return info
+
+
+def get_random_sample_prepare(project_name):
+    name_project = project_name + "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+    file_arrow = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_arrow):
+        return "", None
+    dataset = Dataset_.from_file(file_arrow)
+    random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
+    text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
+    audio_path = random_sample["audio_path"][0]
+    return text, audio_path
+
+
+def get_random_sample_transcribe(project_name):
+    name_project = project_name + "_pinyin"
+    path_project = os.path.join(path_data, name_project)
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    if not os.path.isfile(file_metadata):
+        return "", None
+
+    data = ""
+    with open(file_metadata, "r", encoding="utf-8") as f:
+        data = f.read()
+
+    list_data = []
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+        list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]])
+
+    if list_data == []:
+        return "", None
+
+    random_item = random.choice(list_data)
+
+    return random_item[1], random_item[0]
+
+
+def get_random_sample_infer(project_name):
+    text, audio = get_random_sample_transcribe(project_name)
+    return (
+        text,
+        text,
+        audio,
+    )
+
+
+def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step):
+    global last_checkpoint, last_device, tts_api
+
+    if not os.path.isfile(file_checkpoint):
+        return None
+
+    if training_process is not None:
+        device_test = "cpu"
+    else:
+        device_test = None
+
+    if last_checkpoint != file_checkpoint or last_device != device_test:
+        if last_checkpoint != file_checkpoint:
+            last_checkpoint = file_checkpoint
+        if last_device != device_test:
+            last_device = device_test
+
+        tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test)
+
+        print("update", device_test, file_checkpoint)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name)
+        return f.name
+
+
+with gr.Blocks() as app:
+    with gr.Row():
+        project_name = gr.Textbox(label="project name", value="my_speak")
+        bt_create = gr.Button("create new project")
+
+    bt_create.click(fn=create_data_project, inputs=[project_name])
+
+    with gr.Tabs():
+        with gr.TabItem("transcribe Data"):
+            ch_manual = gr.Checkbox(label="user", value=False)
+
+            mark_info_transcribe = gr.Markdown(
+                """```plaintext    
+     Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory. 
+                 
+     my_speak/
+     │
+     └── dataset/
+         ├── audio1.wav
+         └── audio2.wav
+         ...
+     ```""",
+                visible=False,
+            )
+
+            audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple")
+            txt_lang = gr.Text(label="Language", value="english")
+            bt_transcribe = bt_create = gr.Button("transcribe")
+            txt_info_transcribe = gr.Text(label="info", value="")
+            bt_transcribe.click(
+                fn=transcribe_all,
+                inputs=[project_name, audio_speaker, txt_lang, ch_manual],
+                outputs=[txt_info_transcribe],
+            )
+            ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
+
+            random_sample_transcribe = gr.Button("random sample")
+
+            with gr.Row():
+                random_text_transcribe = gr.Text(label="Text")
+                random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_transcribe.click(
+                fn=get_random_sample_transcribe,
+                inputs=[project_name],
+                outputs=[random_text_transcribe, random_audio_transcribe],
+            )
+
+        with gr.TabItem("prepare Data"):
+            gr.Markdown(
+                """```plaintext    
+     place all your wavs folder and your metadata.csv file in {your name project}                                 
+     my_speak/
+     │
+     ├── wavs/
+     │   ├── audio1.wav
+     │   └── audio2.wav
+     |   ...
+     │
+     └── metadata.csv
+      
+     file format metadata.csv
+
+     audio1|text1
+     audio2|text1
+     ...
+
+     ```"""
+            )
+
+            bt_prepare = bt_create = gr.Button("prepare")
+            txt_info_prepare = gr.Text(label="info", value="")
+            bt_prepare.click(fn=create_metadata, inputs=[project_name], outputs=[txt_info_prepare])
+
+            random_sample_prepare = gr.Button("random sample")
+
+            with gr.Row():
+                random_text_prepare = gr.Text(label="Pinyin")
+                random_audio_prepare = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_prepare.click(
+                fn=get_random_sample_prepare, inputs=[project_name], outputs=[random_text_prepare, random_audio_prepare]
+            )
+
+        with gr.TabItem("train Data"):
+            with gr.Row():
+                bt_calculate = bt_create = gr.Button("Auto Settings")
+                ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True)
+                lb_samples = gr.Label(label="samples")
+                batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
+
+            with gr.Row():
+                exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
+                learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
+
+            with gr.Row():
+                batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
+                max_samples = gr.Number(label="Max Samples", value=64)
+
+            with gr.Row():
+                grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
+                max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
+
+            with gr.Row():
+                epochs = gr.Number(label="Epochs", value=10)
+                num_warmup_updates = gr.Number(label="Warmup Updates", value=5)
+
+            with gr.Row():
+                save_per_updates = gr.Number(label="Save per Updates", value=10)
+                last_per_steps = gr.Number(label="Last per Steps", value=50)
+
+            with gr.Row():
+                start_button = gr.Button("Start Training")
+                stop_button = gr.Button("Stop Training", interactive=False)
+
+            txt_info_train = gr.Text(label="info", value="")
+            start_button.click(
+                fn=start_training,
+                inputs=[
+                    project_name,
+                    exp_name,
+                    learning_rate,
+                    batch_size_per_gpu,
+                    batch_size_type,
+                    max_samples,
+                    grad_accumulation_steps,
+                    max_grad_norm,
+                    epochs,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                ],
+                outputs=[txt_info_train, start_button, stop_button],
+            )
+            stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
+            bt_calculate.click(
+                fn=calculate_train,
+                inputs=[
+                    project_name,
+                    batch_size_type,
+                    max_samples,
+                    learning_rate,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                ],
+                outputs=[
+                    batch_size_per_gpu,
+                    max_samples,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    lb_samples,
+                    learning_rate,
+                ],
+            )
+
+        with gr.TabItem("reduse checkpoint"):
+            txt_path_checkpoint = gr.Text(label="path checkpoint :")
+            txt_path_checkpoint_small = gr.Text(label="path output :")
+            txt_info_reduse = gr.Text(label="info", value="")
+            reduse_button = gr.Button("reduse")
+            reduse_button.click(
+                fn=extract_and_save_ema_model,
+                inputs=[txt_path_checkpoint, txt_path_checkpoint_small],
+                outputs=[txt_info_reduse],
+            )
+
+        with gr.TabItem("vocab check experiment"):
+            check_button = gr.Button("check vocab")
+            txt_info_check = gr.Text(label="info", value="")
+            check_button.click(fn=vocab_check, inputs=[project_name], outputs=[txt_info_check])
+
+        with gr.TabItem("test model"):
+            exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+            nfe_step = gr.Number(label="n_step", value=32)
+            file_checkpoint_pt = gr.Textbox(label="Checkpoint", value="")
+
+            random_sample_infer = gr.Button("random sample")
+
+            ref_text = gr.Textbox(label="ref text")
+            ref_audio = gr.Audio(label="audio ref", type="filepath")
+            gen_text = gr.Textbox(label="gen text")
+            random_sample_infer.click(
+                fn=get_random_sample_infer, inputs=[project_name], outputs=[ref_text, gen_text, ref_audio]
+            )
+            check_button_infer = gr.Button("infer")
+            gen_audio = gr.Audio(label="audio gen", type="filepath")
+
+            check_button_infer.click(
+                fn=infer,
+                inputs=[file_checkpoint_pt, exp_name, ref_text, ref_audio, gen_text, nfe_step],
+                outputs=[gen_audio],
+            )
+
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+def main(port, host, share, api):
+    global app
+    print("Starting app...")
+    app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
+
+
+if __name__ == "__main__":
+    main()

gradio_app.py

@@ -0,0 +1,824 @@
+import os
+import re
+import torch
+import torchaudio
+import gradio as gr
+import numpy as np
+import tempfile
+from einops import rearrange
+from vocos import Vocos
+from pydub import AudioSegment, silence
+from model import CFM, UNetT, DiT, MMDiT
+from cached_path import cached_path
+from model.utils import (
+    load_checkpoint,
+    get_tokenizer,
+    convert_char_to_pinyin,
+    save_spectrogram,
+)
+from transformers import pipeline
+import librosa
+import click
+import soundfile as sf
+
+try:
+    import spaces
+    USING_SPACES = True
+except ImportError:
+    USING_SPACES = False
+
+def gpu_decorator(func):
+    if USING_SPACES:
+        return spaces.GPU(func)
+    else:
+        return func
+
+
+
+SPLIT_WORDS = [
+    "but", "however", "nevertheless", "yet", "still",
+    "therefore", "thus", "hence", "consequently",
+    "moreover", "furthermore", "additionally",
+    "meanwhile", "alternatively", "otherwise",
+    "namely", "specifically", "for example", "such as",
+    "in fact", "indeed", "notably",
+    "in contrast", "on the other hand", "conversely",
+    "in conclusion", "to summarize", "finally"
+]
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "mps" if torch.backends.mps.is_available() else "cpu"
+)
+
+print(f"Using {device} device")
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-large-v3-turbo",
+    torch_dtype=torch.float16,
+    device=device,
+)
+vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+# --------------------- Settings -------------------- #
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+target_rms = 0.1
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+ode_method = "euler"
+sway_sampling_coef = -1.0
+speed = 1.0
+# fix_duration = 27  # None or float (duration in seconds)
+fix_duration = None
+
+
+def load_model(repo_name, exp_name, model_cls, model_cfg, ckpt_step):
+    ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+    # ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors
+    vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin")
+    model = CFM(
+        transformer=model_cls(
+            **model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels
+        ),
+        mel_spec_kwargs=dict(
+            target_sample_rate=target_sample_rate,
+            n_mel_channels=n_mel_channels,
+            hop_length=hop_length,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    model = load_checkpoint(model, ckpt_path, device, use_ema = True)
+
+    return model
+
+
+# load models
+F5TTS_model_cfg = dict(
+    dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4
+)
+E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+
+F5TTS_ema_model = load_model(
+    "F5-TTS", "F5TTS_Base", DiT, F5TTS_model_cfg, 1200000
+)
+E2TTS_ema_model = load_model(
+    "E2-TTS", "E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000
+)
+
+def split_text_into_batches(text, max_chars=200, split_words=SPLIT_WORDS):
+    if len(text.encode('utf-8')) <= max_chars:
+        return [text]
+    if text[-1] not in ['。', '.', '!', '！', '?', '？']:
+        text += '.'
+        
+    sentences = re.split('([。.!?！？])', text)
+    sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2])]
+    
+    batches = []
+    current_batch = ""
+    
+    def split_by_words(text):
+        words = text.split()
+        current_word_part = ""
+        word_batches = []
+        for word in words:
+            if len(current_word_part.encode('utf-8')) + len(word.encode('utf-8')) + 1 <= max_chars:
+                current_word_part += word + ' '
+            else:
+                if current_word_part:
+                    # Try to find a suitable split word
+                    for split_word in split_words:
+                        split_index = current_word_part.rfind(' ' + split_word + ' ')
+                        if split_index != -1:
+                            word_batches.append(current_word_part[:split_index].strip())
+                            current_word_part = current_word_part[split_index:].strip() + ' '
+                            break
+                    else:
+                        # If no suitable split word found, just append the current part
+                        word_batches.append(current_word_part.strip())
+                        current_word_part = ""
+                current_word_part += word + ' '
+        if current_word_part:
+            word_batches.append(current_word_part.strip())
+        return word_batches
+
+    for sentence in sentences:
+        if len(current_batch.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
+            current_batch += sentence
+        else:
+            # If adding this sentence would exceed the limit
+            if current_batch:
+                batches.append(current_batch)
+                current_batch = ""
+            
+            # If the sentence itself is longer than max_chars, split it
+            if len(sentence.encode('utf-8')) > max_chars:
+                # First, try to split by colon
+                colon_parts = sentence.split(':')
+                if len(colon_parts) > 1:
+                    for part in colon_parts:
+                        if len(part.encode('utf-8')) <= max_chars:
+                            batches.append(part)
+                        else:
+                            # If colon part is still too long, split by comma
+                            comma_parts = re.split('[,，]', part)
+                            if len(comma_parts) > 1:
+                                current_comma_part = ""
+                                for comma_part in comma_parts:
+                                    if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
+                                        current_comma_part += comma_part + ','
+                                    else:
+                                        if current_comma_part:
+                                            batches.append(current_comma_part.rstrip(','))
+                                        current_comma_part = comma_part + ','
+                                if current_comma_part:
+                                    batches.append(current_comma_part.rstrip(','))
+                            else:
+                                # If no comma, split by words
+                                batches.extend(split_by_words(part))
+                else:
+                    # If no colon, split by comma
+                    comma_parts = re.split('[,，]', sentence)
+                    if len(comma_parts) > 1:
+                        current_comma_part = ""
+                        for comma_part in comma_parts:
+                            if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars:
+                                current_comma_part += comma_part + ','
+                            else:
+                                if current_comma_part:
+                                    batches.append(current_comma_part.rstrip(','))
+                                current_comma_part = comma_part + ','
+                        if current_comma_part:
+                            batches.append(current_comma_part.rstrip(','))
+                    else:
+                        # If no comma, split by words
+                        batches.extend(split_by_words(sentence))
+            else:
+                current_batch = sentence
+    
+    if current_batch:
+        batches.append(current_batch)
+    
+    return batches
+
+def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, progress=gr.Progress()):
+    if exp_name == "F5-TTS":
+        ema_model = F5TTS_ema_model
+    elif exp_name == "E2-TTS":
+        ema_model = E2TTS_ema_model
+
+    audio, sr = ref_audio
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    rms = torch.sqrt(torch.mean(torch.square(audio)))
+    if rms < target_rms:
+        audio = audio * target_rms / rms
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+        audio = resampler(audio)
+    audio = audio.to(device)
+
+    generated_waves = []
+    spectrograms = []
+
+    for i, gen_text in enumerate(progress.tqdm(gen_text_batches)):
+        # Prepare the text
+        if len(ref_text[-1].encode('utf-8')) == 1:
+            ref_text = ref_text + " "
+        text_list = [ref_text + gen_text]
+        final_text_list = convert_char_to_pinyin(text_list)
+
+        # Calculate duration
+        ref_audio_len = audio.shape[-1] // hop_length
+        zh_pause_punc = r"。，、；：？！"
+        ref_text_len = len(ref_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, ref_text))
+        gen_text_len = len(gen_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, gen_text))
+        duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
+
+        # inference
+        with torch.inference_mode():
+            generated, _ = ema_model.sample(
+                cond=audio,
+                text=final_text_list,
+                duration=duration,
+                steps=nfe_step,
+                cfg_strength=cfg_strength,
+                sway_sampling_coef=sway_sampling_coef,
+            )
+
+        generated = generated[:, ref_audio_len:, :]
+        generated_mel_spec = rearrange(generated, "1 n d -> 1 d n")
+        generated_wave = vocos.decode(generated_mel_spec.cpu())
+        if rms < target_rms:
+            generated_wave = generated_wave * rms / target_rms
+
+        # wav -> numpy
+        generated_wave = generated_wave.squeeze().cpu().numpy()
+        
+        generated_waves.append(generated_wave)
+        spectrograms.append(generated_mel_spec[0].cpu().numpy())
+
+    # Combine all generated waves
+    final_wave = np.concatenate(generated_waves)
+
+    # Remove silence
+    if remove_silence:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+            sf.write(f.name, final_wave, target_sample_rate)
+            aseg = AudioSegment.from_file(f.name)
+            non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
+            non_silent_wave = AudioSegment.silent(duration=0)
+            for non_silent_seg in non_silent_segs:
+                non_silent_wave += non_silent_seg
+            aseg = non_silent_wave
+            aseg.export(f.name, format="wav")
+            final_wave, _ = torchaudio.load(f.name)
+        final_wave = final_wave.squeeze().cpu().numpy()
+
+    # Create a combined spectrogram
+    combined_spectrogram = np.concatenate(spectrograms, axis=1)
+    
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
+        spectrogram_path = tmp_spectrogram.name
+        save_spectrogram(combined_spectrogram, spectrogram_path)
+
+    return (target_sample_rate, final_wave), spectrogram_path
+
+def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, custom_split_words=''):
+    if not custom_split_words.strip():
+        custom_words = [word.strip() for word in custom_split_words.split(',')]
+        global SPLIT_WORDS
+        SPLIT_WORDS = custom_words
+
+    print(gen_text)
+
+    gr.Info("Converting audio...")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        aseg = AudioSegment.from_file(ref_audio_orig)
+
+        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
+        non_silent_wave = AudioSegment.silent(duration=0)
+        for non_silent_seg in non_silent_segs:
+            non_silent_wave += non_silent_seg
+        aseg = non_silent_wave
+
+        audio_duration = len(aseg)
+        if audio_duration > 15000:
+            gr.Warning("Audio is over 15s, clipping to only first 15s.")
+            aseg = aseg[:15000]
+        aseg.export(f.name, format="wav")
+        ref_audio = f.name
+
+    if not ref_text.strip():
+        gr.Info("No reference text provided, transcribing reference audio...")
+        ref_text = pipe(
+            ref_audio,
+            chunk_length_s=30,
+            batch_size=128,
+            generate_kwargs={"task": "transcribe"},
+            return_timestamps=False,
+        )["text"].strip()
+        gr.Info("Finished transcription")
+    else:
+        gr.Info("Using custom reference text...")
+
+    # Split the input text into batches
+    audio, sr = torchaudio.load(ref_audio)
+    max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (30 - audio.shape[-1] / sr))
+    gen_text_batches = split_text_into_batches(gen_text, max_chars=max_chars)
+    print('ref_text', ref_text)
+    for i, gen_text in enumerate(gen_text_batches):
+        print(f'gen_text {i}', gen_text)
+    
+    gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches")
+    return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence)
+    
+def generate_podcast(script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, exp_name, remove_silence):
+    # Split the script into speaker blocks
+    speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE)
+    speaker_blocks = speaker_pattern.split(script)[1:]  # Skip the first empty element
+    
+    generated_audio_segments = []
+    
+    for i in range(0, len(speaker_blocks), 2):
+        speaker = speaker_blocks[i]
+        text = speaker_blocks[i+1].strip()
+        
+        # Determine which speaker is talking
+        if speaker == speaker1_name:
+            ref_audio = ref_audio1
+            ref_text = ref_text1
+        elif speaker == speaker2_name:
+            ref_audio = ref_audio2
+            ref_text = ref_text2
+        else:
+            continue  # Skip if the speaker is neither speaker1 nor speaker2
+        
+        # Generate audio for this block
+        audio, _ = infer(ref_audio, ref_text, text, exp_name, remove_silence)
+        
+        # Convert the generated audio to a numpy array
+        sr, audio_data = audio
+        
+        # Save the audio data as a WAV file
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+            sf.write(temp_file.name, audio_data, sr)
+            audio_segment = AudioSegment.from_wav(temp_file.name)
+        
+        generated_audio_segments.append(audio_segment)
+        
+        # Add a short pause between speakers
+        pause = AudioSegment.silent(duration=500)  # 500ms pause
+        generated_audio_segments.append(pause)
+    
+    # Concatenate all audio segments
+    final_podcast = sum(generated_audio_segments)
+    
+    # Export the final podcast
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
+        podcast_path = temp_file.name
+        final_podcast.export(podcast_path, format="wav")
+    
+    return podcast_path
+
+def parse_speechtypes_text(gen_text):
+    # Pattern to find (Emotion)
+    pattern = r'\((.*?)\)'
+
+    # Split the text by the pattern
+    tokens = re.split(pattern, gen_text)
+
+    segments = []
+
+    current_emotion = 'Regular'
+
+    for i in range(len(tokens)):
+        if i % 2 == 0:
+            # This is text
+            text = tokens[i].strip()
+            if text:
+                segments.append({'emotion': current_emotion, 'text': text})
+        else:
+            # This is emotion
+            emotion = tokens[i].strip()
+            current_emotion = emotion
+
+    return segments
+
+def update_speed(new_speed):
+    global speed
+    speed = new_speed
+    return f"Speed set to: {speed}"
+
+with gr.Blocks() as app_credits:
+    gr.Markdown("""
+# Credits
+
+* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
+* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation
+""")
+with gr.Blocks() as app_tts:
+    gr.Markdown("# Batched TTS")
+    ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
+    gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
+    model_choice = gr.Radio(
+        choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
+    )
+    generate_btn = gr.Button("Synthesize", variant="primary")
+    with gr.Accordion("Advanced Settings", open=False):
+        ref_text_input = gr.Textbox(
+            label="Reference Text",
+            info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.",
+            lines=2,
+        )
+        remove_silence = gr.Checkbox(
+            label="Remove Silences",
+            info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.",
+            value=True,
+        )
+        split_words_input = gr.Textbox(
+            label="Custom Split Words",
+            info="Enter custom words to split on, separated by commas. Leave blank to use default list.",
+            lines=2,
+        )
+        speed_slider = gr.Slider(
+            label="Speed",
+            minimum=0.3,
+            maximum=2.0,
+            value=speed,
+            step=0.1,
+            info="Adjust the speed of the audio.",
+        )
+    speed_slider.change(update_speed, inputs=speed_slider)
+
+    audio_output = gr.Audio(label="Synthesized Audio")
+    spectrogram_output = gr.Image(label="Spectrogram")
+
+    generate_btn.click(
+        infer,
+        inputs=[
+            ref_audio_input,
+            ref_text_input,
+            gen_text_input,
+            model_choice,
+            remove_silence,
+            split_words_input,
+        ],
+        outputs=[audio_output, spectrogram_output],
+    )
+    
+with gr.Blocks() as app_podcast:
+    gr.Markdown("# Podcast Generation")
+    speaker1_name = gr.Textbox(label="Speaker 1 Name")
+    ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath")
+    ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2)
+    
+    speaker2_name = gr.Textbox(label="Speaker 2 Name")
+    ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath")
+    ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2)
+    
+    script_input = gr.Textbox(label="Podcast Script", lines=10, 
+                                placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...")
+    
+    podcast_model_choice = gr.Radio(
+        choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
+    )
+    podcast_remove_silence = gr.Checkbox(
+        label="Remove Silences",
+        value=True,
+    )
+    generate_podcast_btn = gr.Button("Generate Podcast", variant="primary")
+    podcast_output = gr.Audio(label="Generated Podcast")
+
+    def podcast_generation(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence):
+        return generate_podcast(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence)
+
+    generate_podcast_btn.click(
+        podcast_generation,
+        inputs=[
+            script_input,
+            speaker1_name,
+            ref_audio_input1,
+            ref_text_input1,
+            speaker2_name,
+            ref_audio_input2,
+            ref_text_input2,
+            podcast_model_choice,
+            podcast_remove_silence,
+        ],
+        outputs=podcast_output,
+    )
+
+def parse_emotional_text(gen_text):
+    # Pattern to find (Emotion)
+    pattern = r'\((.*?)\)'
+
+    # Split the text by the pattern
+    tokens = re.split(pattern, gen_text)
+
+    segments = []
+
+    current_emotion = 'Regular'
+
+    for i in range(len(tokens)):
+        if i % 2 == 0:
+            # This is text
+            text = tokens[i].strip()
+            if text:
+                segments.append({'emotion': current_emotion, 'text': text})
+        else:
+            # This is emotion
+            emotion = tokens[i].strip()
+            current_emotion = emotion
+
+    return segments
+
+with gr.Blocks() as app_emotional:
+    # New section for emotional generation
+    gr.Markdown(
+        """
+    # Multiple Speech-Type Generation
+
+    This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified.
+
+    **Example Input:**
+
+    (Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?!
+    """
+    )
+
+    gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.")
+
+    # Regular speech type (mandatory)
+    with gr.Row():
+        regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False)
+        regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath')
+        regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2)
+
+    # Additional speech types (up to 9 more)
+    max_speech_types = 10
+    speech_type_names = []
+    speech_type_audios = []
+    speech_type_ref_texts = []
+    speech_type_delete_btns = []
+
+    for i in range(max_speech_types - 1):
+        with gr.Row():
+            name_input = gr.Textbox(label='Speech Type Name', visible=False)
+            audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False)
+            ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False)
+            delete_btn = gr.Button("Delete", variant="secondary", visible=False)
+        speech_type_names.append(name_input)
+        speech_type_audios.append(audio_input)
+        speech_type_ref_texts.append(ref_text_input)
+        speech_type_delete_btns.append(delete_btn)
+
+    # Button to add speech type
+    add_speech_type_btn = gr.Button("Add Speech Type")
+
+    # Keep track of current number of speech types
+    speech_type_count = gr.State(value=0)
+
+    # Function to add a speech type
+    def add_speech_type_fn(speech_type_count):
+        if speech_type_count < max_speech_types - 1:
+            speech_type_count += 1
+            # Prepare updates for the components
+            name_updates = []
+            audio_updates = []
+            ref_text_updates = []
+            delete_btn_updates = []
+            for i in range(max_speech_types - 1):
+                if i < speech_type_count:
+                    name_updates.append(gr.update(visible=True))
+                    audio_updates.append(gr.update(visible=True))
+                    ref_text_updates.append(gr.update(visible=True))
+                    delete_btn_updates.append(gr.update(visible=True))
+                else:
+                    name_updates.append(gr.update())
+                    audio_updates.append(gr.update())
+                    ref_text_updates.append(gr.update())
+                    delete_btn_updates.append(gr.update())
+        else:
+            # Optionally, show a warning
+            # gr.Warning("Maximum number of speech types reached.")
+            name_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            audio_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)]
+            delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)]
+        return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
+
+    add_speech_type_btn.click(
+        add_speech_type_fn,
+        inputs=speech_type_count,
+        outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
+    )
+
+    # Function to delete a speech type
+    def make_delete_speech_type_fn(index):
+        def delete_speech_type_fn(speech_type_count):
+            # Prepare updates
+            name_updates = []
+            audio_updates = []
+            ref_text_updates = []
+            delete_btn_updates = []
+
+            for i in range(max_speech_types - 1):
+                if i == index:
+                    name_updates.append(gr.update(visible=False, value=''))
+                    audio_updates.append(gr.update(visible=False, value=None))
+                    ref_text_updates.append(gr.update(visible=False, value=''))
+                    delete_btn_updates.append(gr.update(visible=False))
+                else:
+                    name_updates.append(gr.update())
+                    audio_updates.append(gr.update())
+                    ref_text_updates.append(gr.update())
+                    delete_btn_updates.append(gr.update())
+
+            speech_type_count = max(0, speech_type_count - 1)
+
+            return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
+
+        return delete_speech_type_fn
+
+    for i, delete_btn in enumerate(speech_type_delete_btns):
+        delete_fn = make_delete_speech_type_fn(i)
+        delete_btn.click(
+            delete_fn,
+            inputs=speech_type_count,
+            outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
+        )
+
+    # Text input for the prompt
+    gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10)
+
+    # Model choice
+    model_choice_emotional = gr.Radio(
+        choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS"
+    )
+
+    with gr.Accordion("Advanced Settings", open=False):
+        remove_silence_emotional = gr.Checkbox(
+            label="Remove Silences",
+            value=True,
+        )
+
+    # Generate button
+    generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary")
+
+    # Output audio
+    audio_output_emotional = gr.Audio(label="Synthesized Audio")
+
+    def generate_emotional_speech(
+        regular_audio,
+        regular_ref_text,
+        gen_text,
+        *args,
+    ):
+        num_additional_speech_types = max_speech_types - 1
+        speech_type_names_list = args[:num_additional_speech_types]
+        speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types]
+        speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types]
+        model_choice = args[3 * num_additional_speech_types]
+        remove_silence = args[3 * num_additional_speech_types + 1]
+
+        # Collect the speech types and their audios into a dict
+        speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}}
+
+        for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list):
+            if name_input and audio_input:
+                speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input}
+
+        # Parse the gen_text into segments
+        segments = parse_speechtypes_text(gen_text)
+
+        # For each segment, generate speech
+        generated_audio_segments = []
+        current_emotion = 'Regular'
+
+        for segment in segments:
+            emotion = segment['emotion']
+            text = segment['text']
+
+            if emotion in speech_types:
+                current_emotion = emotion
+            else:
+                # If emotion not available, default to Regular
+                current_emotion = 'Regular'
+
+            ref_audio = speech_types[current_emotion]['audio']
+            ref_text = speech_types[current_emotion].get('ref_text', '')
+
+            # Generate speech for this segment
+            audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, "")
+            sr, audio_data = audio
+
+            generated_audio_segments.append(audio_data)
+
+        # Concatenate all audio segments
+        if generated_audio_segments:
+            final_audio_data = np.concatenate(generated_audio_segments)
+            return (sr, final_audio_data)
+        else:
+            gr.Warning("No audio generated.")
+            return None
+
+    generate_emotional_btn.click(
+        generate_emotional_speech,
+        inputs=[
+            regular_audio,
+            regular_ref_text,
+            gen_text_input_emotional,
+        ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [
+            model_choice_emotional,
+            remove_silence_emotional,
+        ],
+        outputs=audio_output_emotional,
+    )
+
+    # Validation function to disable Generate button if speech types are missing
+    def validate_speech_types(
+        gen_text,
+        regular_name,
+        *args
+    ):
+        num_additional_speech_types = max_speech_types - 1
+        speech_type_names_list = args[:num_additional_speech_types]
+
+        # Collect the speech types names
+        speech_types_available = set()
+        if regular_name:
+            speech_types_available.add(regular_name)
+        for name_input in speech_type_names_list:
+            if name_input:
+                speech_types_available.add(name_input)
+
+        # Parse the gen_text to get the speech types used
+        segments = parse_emotional_text(gen_text)
+        speech_types_in_text = set(segment['emotion'] for segment in segments)
+
+        # Check if all speech types in text are available
+        missing_speech_types = speech_types_in_text - speech_types_available
+
+        if missing_speech_types:
+            # Disable the generate button
+            return gr.update(interactive=False)
+        else:
+            # Enable the generate button
+            return gr.update(interactive=True)
+
+    gen_text_input_emotional.change(
+        validate_speech_types,
+        inputs=[gen_text_input_emotional, regular_name] + speech_type_names,
+        outputs=generate_emotional_btn
+    )
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+# E2/F5 TTS
+
+This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
+
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
+
+The checkpoints support English and Chinese.
+
+If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt.
+
+**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
+"""
+    )
+    gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"])
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+def main(port, host, share, api):
+    global app
+    print(f"Starting app...")
+    app.queue(api_open=api).launch(
+        server_name=host, server_port=port, share=share, show_api=api
+    )
+
+
+if __name__ == "__main__":
+    main()

inference-cli.py

@@ -0,0 +1,170 @@
+import argparse
+import codecs
+import re
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tomli
+from cached_path import cached_path
+
+from model import DiT, UNetT
+from model.utils_infer import (
+    load_vocoder,
+    load_model,
+    preprocess_ref_audio_text,
+    infer_process,
+    remove_silence_for_generated_wav,
+)
+
+
+parser = argparse.ArgumentParser(
+    prog="python3 inference-cli.py",
+    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
+    epilog="Specify  options above  to override  one or more settings from config.",
+)
+parser.add_argument(
+    "-c",
+    "--config",
+    help="Configuration file. Default=cli-config.toml",
+    default="inference-cli.toml",
+)
+parser.add_argument(
+    "-m",
+    "--model",
+    help="F5-TTS | E2-TTS",
+)
+parser.add_argument(
+    "-p",
+    "--ckpt_file",
+    help="The Checkpoint .pt",
+)
+parser.add_argument(
+    "-v",
+    "--vocab_file",
+    help="The vocab .txt",
+)
+parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.")
+parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.")
+parser.add_argument(
+    "-t",
+    "--gen_text",
+    type=str,
+    help="Text to generate.",
+)
+parser.add_argument(
+    "-f",
+    "--gen_file",
+    type=str,
+    help="File with text to generate. Ignores --text",
+)
+parser.add_argument(
+    "-o",
+    "--output_dir",
+    type=str,
+    help="Path to output folder..",
+)
+parser.add_argument(
+    "--remove_silence",
+    help="Remove silence.",
+)
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
+)
+args = parser.parse_args()
+
+config = tomli.load(open(args.config, "rb"))
+
+ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
+ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
+gen_text = args.gen_text if args.gen_text else config["gen_text"]
+gen_file = args.gen_file if args.gen_file else config["gen_file"]
+if gen_file:
+    gen_text = codecs.open(gen_file, "r", "utf-8").read()
+output_dir = args.output_dir if args.output_dir else config["output_dir"]
+model = args.model if args.model else config["model"]
+ckpt_file = args.ckpt_file if args.ckpt_file else ""
+vocab_file = args.vocab_file if args.vocab_file else ""
+remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
+wave_path = Path(output_dir) / "out.wav"
+spectrogram_path = Path(output_dir) / "out.png"
+vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+
+vocos = load_vocoder(is_local=args.load_vocoder_from_local, local_path=vocos_local_path)
+
+
+# load models
+if model == "F5-TTS":
+    model_cls = DiT
+    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+    if ckpt_file == "":
+        repo_name = "F5-TTS"
+        exp_name = "F5TTS_Base"
+        ckpt_step = 1200000
+        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
+
+elif model == "E2-TTS":
+    model_cls = UNetT
+    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+    if ckpt_file == "":
+        repo_name = "E2-TTS"
+        exp_name = "E2TTS_Base"
+        ckpt_step = 1200000
+        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
+
+print(f"Using {model}...")
+ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file)
+
+
+def main_process(ref_audio, ref_text, text_gen, model_obj, remove_silence):
+    main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
+    if "voices" not in config:
+        voices = {"main": main_voice}
+    else:
+        voices = config["voices"]
+        voices["main"] = main_voice
+    for voice in voices:
+        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
+            voices[voice]["ref_audio"], voices[voice]["ref_text"]
+        )
+        print("Voice:", voice)
+        print("Ref_audio:", voices[voice]["ref_audio"])
+        print("Ref_text:", voices[voice]["ref_text"])
+
+    generated_audio_segments = []
+    reg1 = r"(?=\[\w+\])"
+    chunks = re.split(reg1, text_gen)
+    reg2 = r"\[(\w+)\]"
+    for text in chunks:
+        match = re.match(reg2, text)
+        if match:
+            voice = match[1]
+        else:
+            print("No voice tag found, using main.")
+            voice = "main"
+        if voice not in voices:
+            print(f"Voice {voice} not found, using main.")
+            voice = "main"
+        text = re.sub(reg2, "", text)
+        gen_text = text.strip()
+        ref_audio = voices[voice]["ref_audio"]
+        ref_text = voices[voice]["ref_text"]
+        print(f"Voice: {voice}")
+        audio, final_sample_rate, spectragram = infer_process(ref_audio, ref_text, gen_text, model_obj)
+        generated_audio_segments.append(audio)
+
+    if generated_audio_segments:
+        final_wave = np.concatenate(generated_audio_segments)
+        with open(wave_path, "wb") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            # Remove silence
+            if remove_silence:
+                remove_silence_for_generated_wav(f.name)
+            print(f.name)
+
+
+main_process(ref_audio, ref_text, gen_text, ema_model, remove_silence)

inference-cli.toml

@@ -0,0 +1,10 @@
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "tests/ref_audio/test_en_1_ref_short.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"

model/__init__.py

@@ -0,0 +1,10 @@
+from model.cfm import CFM
+
+from model.backbones.unett import UNetT
+from model.backbones.dit import DiT
+from model.backbones.mmdit import MMDiT
+
+from model.trainer import Trainer
+
+
+__all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"]

model/backbones/README.md

@@ -0,0 +1,20 @@
+## Backbones quick introduction
+
+
+### unett.py
+- flat unet transformer
+- structure same as in e2-tts & voicebox paper except using rotary pos emb
+- update: allow possible abs pos emb & convnextv2 blocks for embedded text before concat
+
+### dit.py
+- adaln-zero dit
+- embedded timestep as condition
+- concatted noised_input + masked_cond + embedded_text, linear proj in
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+- possible long skip connection (first layer to last layer)
+
+### mmdit.py
+- sd3 structure
+- timestep as condition
+- left stream: text embedded and applied a abs pos emb
+- right stream: masked_cond & noised_input concatted and with same conv pos emb as unett

model/backbones/dit.py

@@ -0,0 +1,163 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from x_transformers.x_transformers import RotaryEmbedding
+
+from model.modules import (
+    TimestepEmbedding,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    DiTBlock,
+    AdaLayerNormZero_Final,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using DiT blocks
+
+
+class DiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        conv_layers=0,
+        long_skip_connection=False,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
+        )
+        self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
+
+        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len)
+
+        if self.long_skip_connection is not None:
+            residual = x
+
+        for block in self.transformer_blocks:
+            x = block(x, t, mask=mask, rope=rope)
+
+        if self.long_skip_connection is not None:
+            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

model/backbones/mmdit.py

@@ -0,0 +1,146 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from x_transformers.x_transformers import RotaryEmbedding
+
+from model.modules import (
+    TimestepEmbedding,
+    ConvPositionEmbedding,
+    MMDiTBlock,
+    AdaLayerNormZero_Final,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, out_dim, text_num_embeds):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token
+
+        self.precompute_max_pos = 1024
+        self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
+
+    def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]:  # noqa: F722
+        text = text + 1
+        if drop_text:
+            text = torch.zeros_like(text)
+        text = self.text_embed(text)
+
+        # sinus pos emb
+        batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
+        batch_text_len = text.shape[1]
+        pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
+        text_pos_embed = self.freqs_cis[pos_idx]
+
+        text = text + text_pos_embed
+
+        return text
+
+
+# noised input & masked cond audio embedding
+
+
+class AudioEmbedding(nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+        self.linear = nn.Linear(2 * in_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:
+            cond = torch.zeros_like(cond)
+        x = torch.cat((x, cond), dim=-1)
+        x = self.linear(x)
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using MM-DiT blocks
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        text_num_embeds=256,
+        mel_dim=100,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        self.text_embed = TextEmbedding(dim, text_num_embeds)
+        self.audio_embed = AudioEmbedding(mel_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MMDiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    dropout=dropout,
+                    ff_mult=ff_mult,
+                    context_pre_only=i == depth - 1,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch = x.shape[0]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        c = self.text_embed(text, drop_text=drop_text)
+        x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
+
+        seq_len = x.shape[1]
+        text_len = text.shape[1]
+        rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
+        rope_text = self.rotary_embed.forward_from_seq_len(text_len)
+
+        for block in self.transformer_blocks:
+            c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text)
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

model/backbones/unett.py

@@ -0,0 +1,219 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Literal
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from x_transformers import RMSNorm
+from x_transformers.x_transformers import RotaryEmbedding
+
+from model.modules import (
+    TimestepEmbedding,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    Attention,
+    AttnProcessor,
+    FeedForward,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Flat UNet Transformer backbone
+
+
+class UNetT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        conv_layers=0,
+        skip_connect_type: Literal["add", "concat", "none"] = "concat",
+    ):
+        super().__init__()
+        assert depth % 2 == 0, "UNet-Transformer's depth should be even."
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        # transformer layers & skip connections
+
+        self.dim = dim
+        self.skip_connect_type = skip_connect_type
+        needs_skip_proj = skip_connect_type == "concat"
+
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        for idx in range(depth):
+            is_later_half = idx >= (depth // 2)
+
+            attn_norm = RMSNorm(dim)
+            attn = Attention(
+                processor=AttnProcessor(),
+                dim=dim,
+                heads=heads,
+                dim_head=dim_head,
+                dropout=dropout,
+            )
+
+            ff_norm = RMSNorm(dim)
+            ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+            skip_proj = nn.Linear(dim * 2, dim, bias=False) if needs_skip_proj and is_later_half else None
+
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        skip_proj,
+                        attn_norm,
+                        attn,
+                        ff_norm,
+                        ff,
+                    ]
+                )
+            )
+
+        self.norm_out = RMSNorm(dim)
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        # postfix time t to input x, [b n d] -> [b n+1 d]
+        x = torch.cat([t.unsqueeze(1), x], dim=1)  # pack t to x
+        if mask is not None:
+            mask = F.pad(mask, (1, 0), value=1)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len + 1)
+
+        # flat unet transformer
+        skip_connect_type = self.skip_connect_type
+        skips = []
+        for idx, (maybe_skip_proj, attn_norm, attn, ff_norm, ff) in enumerate(self.layers):
+            layer = idx + 1
+
+            # skip connection logic
+            is_first_half = layer <= (self.depth // 2)
+            is_later_half = not is_first_half
+
+            if is_first_half:
+                skips.append(x)
+
+            if is_later_half:
+                skip = skips.pop()
+                if skip_connect_type == "concat":
+                    x = torch.cat((x, skip), dim=-1)
+                    x = maybe_skip_proj(x)
+                elif skip_connect_type == "add":
+                    x = x + skip
+
+            # attention and feedforward blocks
+            x = attn(attn_norm(x), rope=rope, mask=mask) + x
+            x = ff(ff_norm(x)) + x
+
+        assert len(skips) == 0
+
+        x = self.norm_out(x)[:, 1:, :]  # unpack t from x
+
+        return self.proj_out(x)

model/cfm.py

@@ -0,0 +1,287 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Callable
+from random import random
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from torchdiffeq import odeint
+
+from model.modules import MelSpec
+from model.utils import (
+    default,
+    exists,
+    list_str_to_idx,
+    list_str_to_tensor,
+    lens_to_mask,
+    mask_from_frac_lengths,
+)
+
+
+class CFM(nn.Module):
+    def __init__(
+        self,
+        transformer: nn.Module,
+        sigma=0.0,
+        odeint_kwargs: dict = dict(
+            # atol = 1e-5,
+            # rtol = 1e-5,
+            method="euler"  # 'midpoint'
+        ),
+        audio_drop_prob=0.3,
+        cond_drop_prob=0.2,
+        num_channels=None,
+        mel_spec_module: nn.Module | None = None,
+        mel_spec_kwargs: dict = dict(),
+        frac_lengths_mask: tuple[float, float] = (0.7, 1.0),
+        vocab_char_map: dict[str:int] | None = None,
+    ):
+        super().__init__()
+
+        self.frac_lengths_mask = frac_lengths_mask
+
+        # mel spec
+        self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs))
+        num_channels = default(num_channels, self.mel_spec.n_mel_channels)
+        self.num_channels = num_channels
+
+        # classifier-free guidance
+        self.audio_drop_prob = audio_drop_prob
+        self.cond_drop_prob = cond_drop_prob
+
+        # transformer
+        self.transformer = transformer
+        dim = transformer.dim
+        self.dim = dim
+
+        # conditional flow related
+        self.sigma = sigma
+
+        # sampling related
+        self.odeint_kwargs = odeint_kwargs
+
+        # vocab map for tokenization
+        self.vocab_char_map = vocab_char_map
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: float["b n d"] | float["b nw"],  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        duration: int | int["b"],  # noqa: F821
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        steps=32,
+        cfg_strength=1.0,
+        sway_sampling_coef=None,
+        seed: int | None = None,
+        max_duration=4096,
+        vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None,  # noqa: F722
+        no_ref_audio=False,
+        duplicate_test=False,
+        t_inter=0.1,
+        edit_mask=None,
+    ):
+        self.eval()
+
+        if next(self.parameters()).dtype == torch.float16:
+            cond = cond.half()
+
+        # raw wave
+
+        if cond.ndim == 2:
+            cond = self.mel_spec(cond)
+            cond = cond.permute(0, 2, 1)
+            assert cond.shape[-1] == self.num_channels
+
+        batch, cond_seq_len, device = *cond.shape[:2], cond.device
+        if not exists(lens):
+            lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long)
+
+        # text
+
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        if exists(text):
+            text_lens = (text != -1).sum(dim=-1)
+            lens = torch.maximum(text_lens, lens)  # make sure lengths are at least those of the text characters
+
+        # duration
+
+        cond_mask = lens_to_mask(lens)
+        if edit_mask is not None:
+            cond_mask = cond_mask & edit_mask
+
+        if isinstance(duration, int):
+            duration = torch.full((batch,), duration, device=device, dtype=torch.long)
+
+        duration = torch.maximum(lens + 1, duration)  # just add one token so something is generated
+        duration = duration.clamp(max=max_duration)
+        max_duration = duration.amax()
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0)
+
+        cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0)
+        cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False)
+        cond_mask = cond_mask.unsqueeze(-1)
+        step_cond = torch.where(
+            cond_mask, cond, torch.zeros_like(cond)
+        )  # allow direct control (cut cond audio) with lens passed in
+
+        if batch > 1:
+            mask = lens_to_mask(duration)
+        else:  # save memory and speed up, as single inference need no mask currently
+            mask = None
+
+        # test for no ref audio
+        if no_ref_audio:
+            cond = torch.zeros_like(cond)
+
+        # neural ode
+
+        def fn(t, x):
+            # at each step, conditioning is fixed
+            # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))
+
+            # predict flow
+            pred = self.transformer(
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=False, drop_text=False
+            )
+            if cfg_strength < 1e-5:
+                return pred
+
+            null_pred = self.transformer(
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=True, drop_text=True
+            )
+            return pred + (pred - null_pred) * cfg_strength
+
+        # noise input
+        # to make sure batch inference result is same with different batch size, and for sure single inference
+        # still some difference maybe due to convolutional layers
+        y0 = []
+        for dur in duration:
+            if exists(seed):
+                torch.manual_seed(seed)
+            y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype))
+        y0 = pad_sequence(y0, padding_value=0, batch_first=True)
+
+        t_start = 0
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            t_start = t_inter
+            y0 = (1 - t_start) * y0 + t_start * test_cond
+            steps = int(steps * (1 - t_start))
+
+        t = torch.linspace(t_start, 1, steps, device=self.device, dtype=step_cond.dtype)
+        if sway_sampling_coef is not None:
+            t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
+
+        trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
+
+        sampled = trajectory[-1]
+        out = sampled
+        out = torch.where(cond_mask, cond, out)
+
+        if exists(vocoder):
+            out = out.permute(0, 2, 1)
+            out = vocoder(out)
+
+        return out, trajectory
+
+    def forward(
+        self,
+        inp: float["b n d"] | float["b nw"],  # mel or raw wave  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        noise_scheduler: str | None = None,
+    ):
+        # handle raw wave
+        if inp.ndim == 2:
+            inp = self.mel_spec(inp)
+            inp = inp.permute(0, 2, 1)
+            assert inp.shape[-1] == self.num_channels
+
+        batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma
+
+        # handle text as string
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # lens and mask
+        if not exists(lens):
+            lens = torch.full((batch,), seq_len, device=device)
+
+        mask = lens_to_mask(lens, length=seq_len)  # useless here, as collate_fn will pad to max length in batch
+
+        # get a random span to mask out for training conditionally
+        frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask)
+        rand_span_mask = mask_from_frac_lengths(lens, frac_lengths)
+
+        if exists(mask):
+            rand_span_mask &= mask
+
+        # mel is x1
+        x1 = inp
+
+        # x0 is gaussian noise
+        x0 = torch.randn_like(x1)
+
+        # time step
+        time = torch.rand((batch,), dtype=dtype, device=self.device)
+        # TODO. noise_scheduler
+
+        # sample xt (φ_t(x) in the paper)
+        t = time.unsqueeze(-1).unsqueeze(-1)
+        φ = (1 - t) * x0 + t * x1
+        flow = x1 - x0
+
+        # only predict what is within the random mask span for infilling
+        cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1)
+
+        # transformer and cfg training with a drop rate
+        drop_audio_cond = random() < self.audio_drop_prob  # p_drop in voicebox paper
+        if random() < self.cond_drop_prob:  # p_uncond in voicebox paper
+            drop_audio_cond = True
+            drop_text = True
+        else:
+            drop_text = False
+
+        # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here
+        # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences
+        pred = self.transformer(
+            x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text
+        )
+
+        # flow matching loss
+        loss = F.mse_loss(pred, flow, reduction="none")
+        loss = loss[rand_span_mask]
+
+        return loss.mean(), cond, pred

model/dataset.py

@@ -0,0 +1,294 @@
+import json
+import random
+from tqdm import tqdm
+
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset, Sampler
+import torchaudio
+from datasets import load_from_disk
+from datasets import Dataset as Dataset_
+from torch import nn
+
+from model.modules import MelSpec
+from model.utils import default
+
+
+class HFDataset(Dataset):
+    def __init__(
+        self,
+        hf_dataset: Dataset,
+        target_sample_rate=24_000,
+        n_mel_channels=100,
+        hop_length=256,
+    ):
+        self.data = hf_dataset
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.mel_spectrogram = MelSpec(
+            target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length
+        )
+
+    def get_frame_len(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+        sample_rate = row["audio"]["sampling_rate"]
+        return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+
+        # logger.info(f"Audio shape: {audio.shape}")
+
+        sample_rate = row["audio"]["sampling_rate"]
+        duration = audio.shape[-1] / sample_rate
+
+        if duration > 30 or duration < 0.3:
+            return self.__getitem__((index + 1) % len(self.data))
+
+        audio_tensor = torch.from_numpy(audio).float()
+
+        if sample_rate != self.target_sample_rate:
+            resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate)
+            audio_tensor = resampler(audio_tensor)
+
+        audio_tensor = audio_tensor.unsqueeze(0)  # 't -> 1 t')
+
+        mel_spec = self.mel_spectrogram(audio_tensor)
+
+        mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        text = row["text"]
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+class CustomDataset(Dataset):
+    def __init__(
+        self,
+        custom_dataset: Dataset,
+        durations=None,
+        target_sample_rate=24_000,
+        hop_length=256,
+        n_mel_channels=100,
+        preprocessed_mel=False,
+        mel_spec_module: nn.Module | None = None,
+    ):
+        self.data = custom_dataset
+        self.durations = durations
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.preprocessed_mel = preprocessed_mel
+
+        if not preprocessed_mel:
+            self.mel_spectrogram = default(
+                mel_spec_module,
+                MelSpec(
+                    target_sample_rate=target_sample_rate,
+                    hop_length=hop_length,
+                    n_mel_channels=n_mel_channels,
+                ),
+            )
+
+    def get_frame_len(self, index):
+        if (
+            self.durations is not None
+        ):  # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
+            return self.durations[index] * self.target_sample_rate / self.hop_length
+        return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio_path = row["audio_path"]
+        text = row["text"]
+        duration = row["duration"]
+
+        if self.preprocessed_mel:
+            mel_spec = torch.tensor(row["mel_spec"])
+
+        else:
+            audio, source_sample_rate = torchaudio.load(audio_path)
+            if audio.shape[0] > 1:
+                audio = torch.mean(audio, dim=0, keepdim=True)
+
+            if duration > 30 or duration < 0.3:
+                return self.__getitem__((index + 1) % len(self.data))
+
+            if source_sample_rate != self.target_sample_rate:
+                resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
+                audio = resampler(audio)
+
+            mel_spec = self.mel_spectrogram(audio)
+            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t')
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+# Dynamic Batch Sampler
+
+
+class DynamicBatchSampler(Sampler[list[int]]):
+    """Extension of Sampler that will do the following:
+    1.  Change the batch size (essentially number of sequences)
+        in a batch to ensure that the total number of frames are less
+        than a certain threshold.
+    2.  Make sure the padding efficiency in the batch is high.
+    """
+
+    def __init__(
+        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False
+    ):
+        self.sampler = sampler
+        self.frames_threshold = frames_threshold
+        self.max_samples = max_samples
+
+        indices, batches = [], []
+        data_source = self.sampler.data_source
+
+        for idx in tqdm(
+            self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration"
+        ):
+            indices.append((idx, data_source.get_frame_len(idx)))
+        indices.sort(key=lambda elem: elem[1])
+
+        batch = []
+        batch_frames = 0
+        for idx, frame_len in tqdm(
+            indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"
+        ):
+            if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples):
+                batch.append(idx)
+                batch_frames += frame_len
+            else:
+                if len(batch) > 0:
+                    batches.append(batch)
+                if frame_len <= self.frames_threshold:
+                    batch = [idx]
+                    batch_frames = frame_len
+                else:
+                    batch = []
+                    batch_frames = 0
+
+        if not drop_last and len(batch) > 0:
+            batches.append(batch)
+
+        del indices
+
+        # if want to have different batches between epochs, may just set a seed and log it in ckpt
+        # cuz during multi-gpu training, although the batch on per gpu not change between epochs, the formed general minibatch is different
+        # e.g. for epoch n, use (random_seed + n)
+        random.seed(random_seed)
+        random.shuffle(batches)
+
+        self.batches = batches
+
+    def __iter__(self):
+        return iter(self.batches)
+
+    def __len__(self):
+        return len(self.batches)
+
+
+# Load dataset
+
+
+def load_dataset(
+    dataset_name: str,
+    tokenizer: str = "pinyin",
+    dataset_type: str = "CustomDataset",
+    audio_type: str = "raw",
+    mel_spec_module: nn.Module | None = None,
+    mel_spec_kwargs: dict = dict(),
+) -> CustomDataset | HFDataset:
+    """
+    dataset_type    - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
+                    - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
+    """
+
+    print("Loading dataset ...")
+
+    if dataset_type == "CustomDataset":
+        if audio_type == "raw":
+            try:
+                train_dataset = load_from_disk(f"data/{dataset_name}_{tokenizer}/raw")
+            except:  # noqa: E722
+                train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/raw.arrow")
+            preprocessed_mel = False
+        elif audio_type == "mel":
+            train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/mel.arrow")
+            preprocessed_mel = True
+        with open(f"data/{dataset_name}_{tokenizer}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset,
+            durations=durations,
+            preprocessed_mel=preprocessed_mel,
+            mel_spec_module=mel_spec_module,
+            **mel_spec_kwargs,
+        )
+
+    elif dataset_type == "CustomDatasetPath":
+        try:
+            train_dataset = load_from_disk(f"{dataset_name}/raw")
+        except:  # noqa: E722
+            train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
+
+        with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs
+        )
+
+    elif dataset_type == "HFDataset":
+        print(
+            "Should manually modify the path of huggingface dataset to your need.\n"
+            + "May also the corresponding script cuz different dataset may have different format."
+        )
+        pre, post = dataset_name.split("_")
+        train_dataset = HFDataset(
+            load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir="./data"),
+        )
+
+    return train_dataset
+
+
+# collation
+
+
+def collate_fn(batch):
+    mel_specs = [item["mel_spec"].squeeze(0) for item in batch]
+    mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
+    max_mel_length = mel_lengths.amax()
+
+    padded_mel_specs = []
+    for spec in mel_specs:  # TODO. maybe records mask for attention here
+        padding = (0, max_mel_length - spec.size(-1))
+        padded_spec = F.pad(spec, padding, value=0)
+        padded_mel_specs.append(padded_spec)
+
+    mel_specs = torch.stack(padded_mel_specs)
+
+    text = [item["text"] for item in batch]
+    text_lengths = torch.LongTensor([len(item) for item in text])
+
+    return dict(
+        mel=mel_specs,
+        mel_lengths=mel_lengths,
+        text=text,
+        text_lengths=text_lengths,
+    )

model/ecapa_tdnn.py

@@ -0,0 +1,330 @@
+# just for speaker similarity evaluation, third-party code
+
+# From https://github.com/microsoft/UniSpeech/blob/main/downstreams/speaker_verification/models/
+# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+""" Res2Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(nn.Conv1d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias))
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            # Order: conv -> relu -> bn
+            sp = self.convs[i](sp)
+            sp = self.bns[i](F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True):
+        super().__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
+#     return nn.Sequential(
+#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
+#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
+#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
+#         SE_Connect(channels)
+#     )
+
+
+class SE_Res2Block(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim):
+        super().__init__()
+        self.Conv1dReluBn1 = Conv1dReluBn(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.Res2Conv1dReluBn = Res2Conv1dReluBn(out_channels, kernel_size, stride, padding, dilation, scale=scale)
+        self.Conv1dReluBn2 = Conv1dReluBn(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+            )
+
+    def forward(self, x):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.Conv1dReluBn1(x)
+        x = self.Res2Conv1dReluBn(x)
+        x = self.Conv1dReluBn2(x)
+        x = self.SE_Connect(x)
+
+        return x + residual
+
+
+""" Attentive weighted mean and standard deviation pooling.
+"""
+
+
+class AttentiveStatsPool(nn.Module):
+    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
+        super().__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(in_dim * 3, attention_channels, kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(in_dim, attention_channels, kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
+        alpha = torch.tanh(self.linear1(x_in))
+        # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(residuals.clamp(min=1e-9))
+        return torch.cat([mean, std], dim=1)
+
+
+class ECAPA_TDNN(nn.Module):
+    def __init__(
+        self,
+        feat_dim=80,
+        channels=512,
+        emb_dim=192,
+        global_context_att=False,
+        feat_type="wavlm_large",
+        sr=16000,
+        feature_selection="hidden_states",
+        update_extract=False,
+        config_path=None,
+    ):
+        super().__init__()
+
+        self.feat_type = feat_type
+        self.feature_selection = feature_selection
+        self.update_extract = update_extract
+        self.sr = sr
+
+        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
+        try:
+            local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main")
+            self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source="local", config_path=config_path)
+        except:  # noqa: E722
+            self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
+
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False
+
+        self.feat_num = self.get_feat_num()
+        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
+
+        if feat_type != "fbank" and feat_type != "mfcc":
+            freeze_list = ["final_proj", "label_embs_concat", "mask_emb", "project_q", "quantizer"]
+            for name, param in self.feature_extract.named_parameters():
+                for freeze_val in freeze_list:
+                    if freeze_val in name:
+                        param.requires_grad = False
+                        break
+
+        if not self.update_extract:
+            for param in self.feature_extract.parameters():
+                param.requires_grad = False
+
+        self.instance_norm = nn.InstanceNorm1d(feat_dim)
+        # self.channels = [channels] * 4 + [channels * 3]
+        self.channels = [channels] * 4 + [1536]
+
+        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            self.channels[0],
+            self.channels[1],
+            kernel_size=3,
+            stride=1,
+            padding=2,
+            dilation=2,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer3 = SE_Res2Block(
+            self.channels[1],
+            self.channels[2],
+            kernel_size=3,
+            stride=1,
+            padding=3,
+            dilation=3,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer4 = SE_Res2Block(
+            self.channels[2],
+            self.channels[3],
+            kernel_size=3,
+            stride=1,
+            padding=4,
+            dilation=4,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+
+        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
+        cat_channels = channels * 3
+        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
+        self.pooling = AttentiveStatsPool(
+            self.channels[-1], attention_channels=128, global_context_att=global_context_att
+        )
+        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
+        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
+
+    def get_feat_num(self):
+        self.feature_extract.eval()
+        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
+        with torch.no_grad():
+            features = self.feature_extract(wav)
+        select_feature = features[self.feature_selection]
+        if isinstance(select_feature, (list, tuple)):
+            return len(select_feature)
+        else:
+            return 1
+
+    def get_feat(self, x):
+        if self.update_extract:
+            x = self.feature_extract([sample for sample in x])
+        else:
+            with torch.no_grad():
+                if self.feat_type == "fbank" or self.feat_type == "mfcc":
+                    x = self.feature_extract(x) + 1e-6  # B x feat_dim x time_len
+                else:
+                    x = self.feature_extract([sample for sample in x])
+
+        if self.feat_type == "fbank":
+            x = x.log()
+
+        if self.feat_type != "fbank" and self.feat_type != "mfcc":
+            x = x[self.feature_selection]
+            if isinstance(x, (list, tuple)):
+                x = torch.stack(x, dim=0)
+            else:
+                x = x.unsqueeze(0)
+            norm_weights = F.softmax(self.feature_weight, dim=-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+            x = (norm_weights * x).sum(dim=0)
+            x = torch.transpose(x, 1, 2) + 1e-6
+
+        x = self.instance_norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.get_feat(x)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        out = F.relu(self.conv(out))
+        out = self.bn(self.pooling(out))
+        out = self.linear(out)
+
+        return out
+
+
+def ECAPA_TDNN_SMALL(
+    feat_dim,
+    emb_dim=256,
+    feat_type="wavlm_large",
+    sr=16000,
+    feature_selection="hidden_states",
+    update_extract=False,
+    config_path=None,
+):
+    return ECAPA_TDNN(
+        feat_dim=feat_dim,
+        channels=512,
+        emb_dim=emb_dim,
+        feat_type=feat_type,
+        sr=sr,
+        feature_selection=feature_selection,
+        update_extract=update_extract,
+        config_path=config_path,
+    )

model/modules.py

@@ -0,0 +1,581 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Optional
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+
+from x_transformers.x_transformers import apply_rotary_pos_emb
+
+
+# raw wav to mel spec
+
+
+class MelSpec(nn.Module):
+    def __init__(
+        self,
+        filter_length=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=100,
+        target_sample_rate=24_000,
+        normalize=False,
+        power=1,
+        norm=None,
+        center=True,
+    ):
+        super().__init__()
+        self.n_mel_channels = n_mel_channels
+
+        self.mel_stft = torchaudio.transforms.MelSpectrogram(
+            sample_rate=target_sample_rate,
+            n_fft=filter_length,
+            win_length=win_length,
+            hop_length=hop_length,
+            n_mels=n_mel_channels,
+            power=power,
+            center=center,
+            normalized=normalize,
+            norm=norm,
+        )
+
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    def forward(self, inp):
+        if len(inp.shape) == 3:
+            inp = inp.squeeze(1)  # 'b 1 nw -> b nw'
+
+        assert len(inp.shape) == 2
+
+        if self.dummy.device != inp.device:
+            self.to(inp.device)
+
+        mel = self.mel_stft(inp)
+        mel = mel.clamp(min=1e-5).log()
+        return mel
+
+
+# sinusoidal position embedding
+
+
+class SinusPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x, scale=1000):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+# convolutional position embedding
+
+
+class ConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):  # noqa: F722
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = self.conv1d(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+# rotary positional embedding related
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def get_pos_embed_indices(start, length, max_pos, scale=1.0):
+    # length = length if isinstance(length, int) else length.max()
+    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
+    pos = (
+        start.unsqueeze(1)
+        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
+    )
+    # avoid extra long error.
+    pos = torch.where(pos < max_pos, pos, max_pos - 1)
+    return pos
+
+
+# Global Response Normalization layer (Instance Normalization ?)
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+# AdaLayerNormZero
+# return with modulated x for attn input, and params for later mlp modulation
+
+
+class AdaLayerNormZero(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 6)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNormZero for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+
+
+class AdaLayerNormZero_Final(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 2)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# FeedForward
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        activation = nn.GELU(approximate=approximate)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
+        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.ff(x)
+
+
+# Attention with possible joint part
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        processor: JointAttnProcessor | AttnProcessor,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only=None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+
+        self.to_q = nn.Linear(dim, self.inner_dim)
+        self.to_k = nn.Linear(dim, self.inner_dim)
+        self.to_v = nn.Linear(dim, self.inner_dim)
+
+        if self.context_dim is not None:
+            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
+            if self.context_pre_only is not None:
+                self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+        if self.context_pre_only is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b n d"] = None,  # context c  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(self, x, mask=mask, rope=rope)
+
+
+# Attention processor
+
+
+class AttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding
+    ) -> torch.FloatTensor:
+        batch_size = x.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = mask
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+
+        return x
+
+
+# Joint Attention processor for MM-DiT
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class JointAttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b nt d"] = None,  # context c, here text # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.FloatTensor:
+        residual = x
+
+        batch_size = c.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # `context` projections.
+        c_query = attn.to_q_c(c)
+        c_key = attn.to_k_c(c)
+        c_value = attn.to_v_c(c)
+
+        # apply rope for context and noised input independently
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+        if c_rope is not None:
+            freqs, xpos_scale = c_rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
+            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
+
+        # attention
+        query = torch.cat([query, c_query], dim=1)
+        key = torch.cat([key, c_key], dim=1)
+        value = torch.cat([value, c_value], dim=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # Split the attention outputs.
+        x, c = (
+            x[:, : residual.shape[1]],
+            x[:, residual.shape[1] :],
+        )
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+        if not attn.context_pre_only:
+            c = attn.to_out_c(c)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)
+
+        return x, c
+
+
+# DiT Block
+
+
+class DiTBlock(nn.Module):
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+        )
+
+        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+
+        # attention
+        attn_output = self.attn(x=norm, mask=mask, rope=rope)
+
+        # process attention output for input x
+        x = x + gate_msa.unsqueeze(1) * attn_output
+
+        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(norm)
+        x = x + gate_mlp.unsqueeze(1) * ff_output
+
+        return x
+
+
+# MMDiT Block https://arxiv.org/abs/2403.03206
+
+
+class MMDiTBlock(nn.Module):
+    r"""
+    modified from diffusers/src/diffusers/models/attention.py
+
+    notes.
+    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
+    _x: noised input related. (right part)
+    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
+    """
+
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False):
+        super().__init__()
+
+        self.context_pre_only = context_pre_only
+
+        self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
+        self.attn_norm_x = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=JointAttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            context_dim=dim,
+            context_pre_only=context_pre_only,
+        )
+
+        if not context_pre_only:
+            self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+        else:
+            self.ff_norm_c = None
+            self.ff_c = None
+        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
+        # pre-norm & modulation for attention input
+        if self.context_pre_only:
+            norm_c = self.attn_norm_c(c, t)
+        else:
+            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
+        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
+
+        # attention
+        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
+
+        # process attention output for context c
+        if self.context_pre_only:
+            c = None
+        else:  # if not last layer
+            c = c + c_gate_msa.unsqueeze(1) * c_attn_output
+
+            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+            c_ff_output = self.ff_c(norm_c)
+            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
+
+        # process attention output for input x
+        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
+
+        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
+        x_ff_output = self.ff_x(norm_x)
+        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
+
+        return c, x
+
+
+# time step conditioning embedding
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, dim, freq_embed_dim=256):
+        super().__init__()
+        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+
+    def forward(self, timestep: float["b"]):  # noqa: F821
+        time_hidden = self.time_embed(timestep)
+        time_hidden = time_hidden.to(timestep.dtype)
+        time = self.time_mlp(time_hidden)  # b d
+        return time

model/trainer.py

@@ -0,0 +1,300 @@
+from __future__ import annotations
+
+import os
+import gc
+from tqdm import tqdm
+import wandb
+
+import torch
+from torch.optim import AdamW
+from torch.utils.data import DataLoader, Dataset, SequentialSampler
+from torch.optim.lr_scheduler import LinearLR, SequentialLR
+
+from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
+
+from ema_pytorch import EMA
+
+from model import CFM
+from model.utils import exists, default
+from model.dataset import DynamicBatchSampler, collate_fn
+
+
+# trainer
+
+
+class Trainer:
+    def __init__(
+        self,
+        model: CFM,
+        epochs,
+        learning_rate,
+        num_warmup_updates=20000,
+        save_per_updates=1000,
+        checkpoint_path=None,
+        batch_size=32,
+        batch_size_type: str = "sample",
+        max_samples=32,
+        grad_accumulation_steps=1,
+        max_grad_norm=1.0,
+        noise_scheduler: str | None = None,
+        duration_predictor: torch.nn.Module | None = None,
+        wandb_project="test_e2-tts",
+        wandb_run_name="test_run",
+        wandb_resume_id: str = None,
+        last_per_steps=None,
+        accelerate_kwargs: dict = dict(),
+        ema_kwargs: dict = dict(),
+        bnb_optimizer: bool = False,
+    ):
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+        logger = "wandb" if wandb.api.api_key else None
+        print(f"Using logger: {logger}")
+
+        self.accelerator = Accelerator(
+            log_with=logger,
+            kwargs_handlers=[ddp_kwargs],
+            gradient_accumulation_steps=grad_accumulation_steps,
+            **accelerate_kwargs,
+        )
+
+        if logger == "wandb":
+            if exists(wandb_resume_id):
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
+            else:
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
+            self.accelerator.init_trackers(
+                project_name=wandb_project,
+                init_kwargs=init_kwargs,
+                config={
+                    "epochs": epochs,
+                    "learning_rate": learning_rate,
+                    "num_warmup_updates": num_warmup_updates,
+                    "batch_size": batch_size,
+                    "batch_size_type": batch_size_type,
+                    "max_samples": max_samples,
+                    "grad_accumulation_steps": grad_accumulation_steps,
+                    "max_grad_norm": max_grad_norm,
+                    "gpus": self.accelerator.num_processes,
+                    "noise_scheduler": noise_scheduler,
+                },
+            )
+
+        self.model = model
+
+        if self.is_main:
+            self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
+
+            self.ema_model.to(self.accelerator.device)
+
+        self.epochs = epochs
+        self.num_warmup_updates = num_warmup_updates
+        self.save_per_updates = save_per_updates
+        self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps)
+        self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts")
+
+        self.batch_size = batch_size
+        self.batch_size_type = batch_size_type
+        self.max_samples = max_samples
+        self.grad_accumulation_steps = grad_accumulation_steps
+        self.max_grad_norm = max_grad_norm
+
+        self.noise_scheduler = noise_scheduler
+
+        self.duration_predictor = duration_predictor
+
+        if bnb_optimizer:
+            import bitsandbytes as bnb
+
+            self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
+        else:
+            self.optimizer = AdamW(model.parameters(), lr=learning_rate)
+        self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
+
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
+
+    def save_checkpoint(self, step, last=False):
+        self.accelerator.wait_for_everyone()
+        if self.is_main:
+            checkpoint = dict(
+                model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
+                optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(),
+                ema_model_state_dict=self.ema_model.state_dict(),
+                scheduler_state_dict=self.scheduler.state_dict(),
+                step=step,
+            )
+            if not os.path.exists(self.checkpoint_path):
+                os.makedirs(self.checkpoint_path)
+            if last:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
+                print(f"Saved last checkpoint at step {step}")
+            else:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt")
+
+    def load_checkpoint(self):
+        if (
+            not exists(self.checkpoint_path)
+            or not os.path.exists(self.checkpoint_path)
+            or not os.listdir(self.checkpoint_path)
+        ):
+            return 0
+
+        self.accelerator.wait_for_everyone()
+        if "model_last.pt" in os.listdir(self.checkpoint_path):
+            latest_checkpoint = "model_last.pt"
+        else:
+            latest_checkpoint = sorted(
+                [f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")],
+                key=lambda x: int("".join(filter(str.isdigit, x))),
+            )[-1]
+        # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
+        checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu")
+
+        if self.is_main:
+            self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"])
+
+        if "step" in checkpoint:
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint["optimizer_state_dict"])
+            if self.scheduler:
+                self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+            step = checkpoint["step"]
+        else:
+            checkpoint["model_state_dict"] = {
+                k.replace("ema_model.", ""): v
+                for k, v in checkpoint["ema_model_state_dict"].items()
+                if k not in ["initted", "step"]
+            }
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            step = 0
+
+        del checkpoint
+        gc.collect()
+        return step
+
+    def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None):
+        if exists(resumable_with_seed):
+            generator = torch.Generator()
+            generator.manual_seed(resumable_with_seed)
+        else:
+            generator = None
+
+        if self.batch_size_type == "sample":
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_size=self.batch_size,
+                shuffle=True,
+                generator=generator,
+            )
+        elif self.batch_size_type == "frame":
+            self.accelerator.even_batches = False
+            sampler = SequentialSampler(train_dataset)
+            batch_sampler = DynamicBatchSampler(
+                sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False
+            )
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_sampler=batch_sampler,
+            )
+        else:
+            raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}")
+
+        #  accelerator.prepare() dispatches batches to devices;
+        #  which means the length of dataloader calculated before, should consider the number of devices
+        warmup_steps = (
+            self.num_warmup_updates * self.accelerator.num_processes
+        )  # consider a fixed warmup steps while using accelerate multi-gpu ddp
+        # otherwise by default with split_batches=False, warmup steps change with num_processes
+        total_steps = len(train_dataloader) * self.epochs / self.grad_accumulation_steps
+        decay_steps = total_steps - warmup_steps
+        warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps)
+        decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps)
+        self.scheduler = SequentialLR(
+            self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps]
+        )
+        train_dataloader, self.scheduler = self.accelerator.prepare(
+            train_dataloader, self.scheduler
+        )  # actual steps = 1 gpu steps / gpus
+        start_step = self.load_checkpoint()
+        global_step = start_step
+
+        if exists(resumable_with_seed):
+            orig_epoch_step = len(train_dataloader)
+            skipped_epoch = int(start_step // orig_epoch_step)
+            skipped_batch = start_step % orig_epoch_step
+            skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
+        else:
+            skipped_epoch = 0
+
+        for epoch in range(skipped_epoch, self.epochs):
+            self.model.train()
+            if exists(resumable_with_seed) and epoch == skipped_epoch:
+                progress_bar = tqdm(
+                    skipped_dataloader,
+                    desc=f"Epoch {epoch+1}/{self.epochs}",
+                    unit="step",
+                    disable=not self.accelerator.is_local_main_process,
+                    initial=skipped_batch,
+                    total=orig_epoch_step,
+                )
+            else:
+                progress_bar = tqdm(
+                    train_dataloader,
+                    desc=f"Epoch {epoch+1}/{self.epochs}",
+                    unit="step",
+                    disable=not self.accelerator.is_local_main_process,
+                )
+
+            for batch in progress_bar:
+                with self.accelerator.accumulate(self.model):
+                    text_inputs = batch["text"]
+                    mel_spec = batch["mel"].permute(0, 2, 1)
+                    mel_lengths = batch["mel_lengths"]
+
+                    # TODO. add duration predictor training
+                    if self.duration_predictor is not None and self.accelerator.is_local_main_process:
+                        dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations"))
+                        self.accelerator.log({"duration loss": dur_loss.item()}, step=global_step)
+
+                    loss, cond, pred = self.model(
+                        mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler
+                    )
+                    self.accelerator.backward(loss)
+
+                    if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
+                        self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+                    self.scheduler.step()
+                    self.optimizer.zero_grad()
+
+                if self.is_main:
+                    self.ema_model.update()
+
+                global_step += 1
+
+                if self.accelerator.is_local_main_process:
+                    self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
+
+                progress_bar.set_postfix(step=str(global_step), loss=loss.item())
+
+                if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
+                    self.save_checkpoint(global_step)
+
+                if global_step % self.last_per_steps == 0:
+                    self.save_checkpoint(global_step, last=True)
+
+        self.save_checkpoint(global_step, last=True)
+
+        self.accelerator.end_training()

model/utils.py

@@ -0,0 +1,620 @@
+from __future__ import annotations
+
+import os
+import math
+import random
+import string
+from tqdm import tqdm
+from collections import defaultdict
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pylab as plt
+
+import torch
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+import torchaudio
+
+import jieba
+from pypinyin import lazy_pinyin, Style
+
+from model.ecapa_tdnn import ECAPA_TDNN_SMALL
+from model.modules import MelSpec
+
+
+# seed everything
+
+
+def seed_everything(seed=0):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+# helpers
+
+
+def exists(v):
+    return v is not None
+
+
+def default(v, d):
+    return v if exists(v) else d
+
+
+# tensor helpers
+
+
+def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
+    if not exists(length):
+        length = t.amax()
+
+    seq = torch.arange(length, device=t.device)
+    return seq[None, :] < t[:, None]
+
+
+def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
+    max_seq_len = seq_len.max().item()
+    seq = torch.arange(max_seq_len, device=start.device).long()
+    start_mask = seq[None, :] >= start[:, None]
+    end_mask = seq[None, :] < end[:, None]
+    return start_mask & end_mask
+
+
+def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.rand_like(frac_lengths)
+    start = (max_start * rand).long().clamp(min=0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+
+def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
+    if not exists(mask):
+        return t.mean(dim=1)
+
+    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
+    num = t.sum(dim=1)
+    den = mask.float().sum(dim=1)
+
+    return num / den.clamp(min=1.0)
+
+
+# simple utf-8 tokenizer, since paper went character based
+def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
+    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
+    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# char tokenizer, based on custom dataset's extracted .txt file
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+) -> int["b nt"]:  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# Get tokenizer
+
+
+def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    if tokenizer in ["pinyin", "char"]:
+        with open(f"data/{dataset_name}_{tokenizer}/vocab.txt", "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"
+
+    elif tokenizer == "byte":
+        vocab_char_map = None
+        vocab_size = 256
+    elif tokenizer == "custom":
+        with open(dataset_name, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    return vocab_char_map, vocab_size
+
+
+# convert char to pinyin
+
+
+def convert_char_to_pinyin(text_list, polyphone=True):
+    final_text_list = []
+    god_knows_why_en_testset_contains_zh_quote = str.maketrans(
+        {"“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # in case librispeech (orig no-pc) test-clean
+    custom_trans = str.maketrans({";": ","})  # add custom trans here, to address oov
+    for text in text_list:
+        char_list = []
+        text = text.translate(god_knows_why_en_testset_contains_zh_quote)
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure chinese characters
+                seg = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for c in seg:
+                    if c not in "。，、；：？！《》【】—…":
+                        char_list.append(" ")
+                    char_list.append(c)
+            else:  # if mixed chinese characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    else:
+                        if c not in "。，、；：？！《》【】—…":
+                            char_list.append(" ")
+                            char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                        else:  # if is zh punc
+                            char_list.append(c)
+        final_text_list.append(char_list)
+
+    return final_text_list
+
+
+# save spectrogram
+def save_spectrogram(spectrogram, path):
+    plt.figure(figsize=(12, 4))
+    plt.imshow(spectrogram, origin="lower", aspect="auto")
+    plt.colorbar()
+    plt.savefig(path)
+    plt.close()
+
+
+# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
+def get_seedtts_testset_metainfo(metalst):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+        metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
+    return metainfo
+
+
+# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
+def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        # ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        # gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+        gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+
+        metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
+
+    return metainfo
+
+
+# padded to max length mel batch
+def padded_mel_batch(ref_mels):
+    max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
+    padded_ref_mels = []
+    for mel in ref_mels:
+        padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
+    return padded_ref_mels
+
+
+# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
+
+
+def get_inference_prompt(
+    metainfo,
+    speed=1.0,
+    tokenizer="pinyin",
+    polyphone=True,
+    target_sample_rate=24000,
+    n_mel_channels=100,
+    hop_length=256,
+    target_rms=0.1,
+    use_truth_duration=False,
+    infer_batch_size=1,
+    num_buckets=200,
+    min_secs=3,
+    max_secs=40,
+):
+    prompts_all = []
+
+    min_tokens = min_secs * target_sample_rate // hop_length
+    max_tokens = max_secs * target_sample_rate // hop_length
+
+    batch_accum = [0] * num_buckets
+    utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
+        [[] for _ in range(num_buckets)] for _ in range(6)
+    )
+
+    mel_spectrogram = MelSpec(
+        target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length
+    )
+
+    for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
+        # Audio
+        ref_audio, ref_sr = torchaudio.load(prompt_wav)
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
+        if ref_rms < target_rms:
+            ref_audio = ref_audio * target_rms / ref_rms
+        assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio)
+
+        # Text
+        if len(prompt_text[-1].encode("utf-8")) == 1:
+            prompt_text = prompt_text + " "
+        text = [prompt_text + gt_text]
+        if tokenizer == "pinyin":
+            text_list = convert_char_to_pinyin(text, polyphone=polyphone)
+        else:
+            text_list = text
+
+        # Duration, mel frame length
+        ref_mel_len = ref_audio.shape[-1] // hop_length
+        if use_truth_duration:
+            gt_audio, gt_sr = torchaudio.load(gt_wav)
+            if gt_sr != target_sample_rate:
+                resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
+                gt_audio = resampler(gt_audio)
+            total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
+
+            # # test vocoder resynthesis
+            # ref_audio = gt_audio
+        else:
+            ref_text_len = len(prompt_text.encode("utf-8"))
+            gen_text_len = len(gt_text.encode("utf-8"))
+            total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
+
+        # to mel spectrogram
+        ref_mel = mel_spectrogram(ref_audio)
+        ref_mel = ref_mel.squeeze(0)
+
+        # deal with batch
+        assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
+        assert (
+            min_tokens <= total_mel_len <= max_tokens
+        ), f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]."
+        bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
+
+        utts[bucket_i].append(utt)
+        ref_rms_list[bucket_i].append(ref_rms)
+        ref_mels[bucket_i].append(ref_mel)
+        ref_mel_lens[bucket_i].append(ref_mel_len)
+        total_mel_lens[bucket_i].append(total_mel_len)
+        final_text_list[bucket_i].extend(text_list)
+
+        batch_accum[bucket_i] += total_mel_len
+
+        if batch_accum[bucket_i] >= infer_batch_size:
+            # print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+            batch_accum[bucket_i] = 0
+            (
+                utts[bucket_i],
+                ref_rms_list[bucket_i],
+                ref_mels[bucket_i],
+                ref_mel_lens[bucket_i],
+                total_mel_lens[bucket_i],
+                final_text_list[bucket_i],
+            ) = [], [], [], [], [], []
+
+    # add residual
+    for bucket_i, bucket_frames in enumerate(batch_accum):
+        if bucket_frames > 0:
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+    # not only leave easy work for last workers
+    random.seed(666)
+    random.shuffle(prompts_all)
+
+    return prompts_all
+
+
+# get wav_res_ref_text of seed-tts test metalst
+# https://github.com/BytedanceSpeech/seed-tts-eval
+
+
+def get_seed_tts_test(metalst, gen_wav_dir, gpus):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+
+        if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
+            continue
+        gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+
+        test_set_.append((gen_wav, prompt_wav, gt_text))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# get librispeech test-clean cross sentence test
+
+
+def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        if eval_ground_truth:
+            gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+            gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+        else:
+            if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
+                raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
+            gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
+
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        test_set_.append((gen_wav, ref_wav, gen_txt))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# load asr model
+
+
+def load_asr_model(lang, ckpt_dir=""):
+    if lang == "zh":
+        from funasr import AutoModel
+
+        model = AutoModel(
+            model=os.path.join(ckpt_dir, "paraformer-zh"),
+            # vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
+            # punc_model = os.path.join(ckpt_dir, "ct-punc"),
+            # spk_model = os.path.join(ckpt_dir, "cam++"),
+            disable_update=True,
+        )  # following seed-tts setting
+    elif lang == "en":
+        from faster_whisper import WhisperModel
+
+        model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+    return model
+
+
+# WER Evaluation, the way Seed-TTS does
+
+
+def run_asr_wer(args):
+    rank, lang, test_set, ckpt_dir = args
+
+    if lang == "zh":
+        import zhconv
+
+        torch.cuda.set_device(rank)
+    elif lang == "en":
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+    else:
+        raise NotImplementedError(
+            "lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
+        )
+
+    asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
+
+    from zhon.hanzi import punctuation
+
+    punctuation_all = punctuation + string.punctuation
+    wers = []
+
+    from jiwer import compute_measures
+
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        if lang == "zh":
+            res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
+            hypo = res[0]["text"]
+            hypo = zhconv.convert(hypo, "zh-cn")
+        elif lang == "en":
+            segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
+            hypo = ""
+            for segment in segments:
+                hypo = hypo + " " + segment.text
+
+        # raw_truth = truth
+        # raw_hypo = hypo
+
+        for x in punctuation_all:
+            truth = truth.replace(x, "")
+            hypo = hypo.replace(x, "")
+
+        truth = truth.replace("  ", " ")
+        hypo = hypo.replace("  ", " ")
+
+        if lang == "zh":
+            truth = " ".join([x for x in truth])
+            hypo = " ".join([x for x in hypo])
+        elif lang == "en":
+            truth = truth.lower()
+            hypo = hypo.lower()
+
+        measures = compute_measures(truth, hypo)
+        wer = measures["wer"]
+
+        # ref_list = truth.split(" ")
+        # subs = measures["substitutions"] / len(ref_list)
+        # dele = measures["deletions"] / len(ref_list)
+        # inse = measures["insertions"] / len(ref_list)
+
+        wers.append(wer)
+
+    return wers
+
+
+# SIM Evaluation
+
+
+def run_sim(args):
+    rank, test_set, ckpt_dir = args
+    device = f"cuda:{rank}"
+
+    model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
+    state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
+    model.load_state_dict(state_dict["model"], strict=False)
+
+    use_gpu = True if torch.cuda.is_available() else False
+    if use_gpu:
+        model = model.cuda(device)
+    model.eval()
+
+    sim_list = []
+    for wav1, wav2, truth in tqdm(test_set):
+        wav1, sr1 = torchaudio.load(wav1)
+        wav2, sr2 = torchaudio.load(wav2)
+
+        resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
+        resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
+        wav1 = resample1(wav1)
+        wav2 = resample2(wav2)
+
+        if use_gpu:
+            wav1 = wav1.cuda(device)
+            wav2 = wav2.cuda(device)
+        with torch.no_grad():
+            emb1 = model(wav1)
+            emb2 = model(wav2)
+
+        sim = F.cosine_similarity(emb1, emb2)[0].item()
+        # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
+        sim_list.append(sim)
+
+    return sim_list
+
+
+# filter func for dirty data with many repetitions
+
+
+def repetition_found(text, length=2, tolerance=10):
+    pattern_count = defaultdict(int)
+    for i in range(len(text) - length + 1):
+        pattern = text[i : i + length]
+        pattern_count[pattern] += 1
+    for pattern, count in pattern_count.items():
+        if count > tolerance:
+            return True
+    return False
+
+
+# load model checkpoint for inference
+
+
+def load_checkpoint(model, ckpt_path, device, use_ema=True):
+    if device == "cuda":
+        model = model.half()
+
+    ckpt_type = ckpt_path.split(".")[-1]
+    if ckpt_type == "safetensors":
+        from safetensors.torch import load_file
+
+        checkpoint = load_file(ckpt_path)
+    else:
+        checkpoint = torch.load(ckpt_path, weights_only=True)
+
+    if use_ema:
+        if ckpt_type == "safetensors":
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+        model.load_state_dict(checkpoint["model_state_dict"])
+    else:
+        if ckpt_type == "safetensors":
+            checkpoint = {"model_state_dict": checkpoint}
+        model.load_state_dict(checkpoint["model_state_dict"])
+
+    return model.to(device)

model/utils_infer.py

@@ -0,0 +1,357 @@
+# A unified script for inference process
+# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format
+
+import re
+import tempfile
+
+import numpy as np
+import torch
+import torchaudio
+import tqdm
+from pydub import AudioSegment, silence
+from transformers import pipeline
+from vocos import Vocos
+
+from model import CFM
+from model.utils import (
+    load_checkpoint,
+    get_tokenizer,
+    convert_char_to_pinyin,
+)
+
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+
+# -----------------------------------------
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+target_rms = 0.1
+cross_fade_duration = 0.15
+ode_method = "euler"
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+sway_sampling_coef = -1.0
+speed = 1.0
+fix_duration = None
+
+# -----------------------------------------
+
+
+# chunk text into smaller pieces
+
+
+def chunk_text(text, max_chars=135):
+    """
+    Splits the input text into chunks, each with a maximum number of characters.
+
+    Args:
+        text (str): The text to be split.
+        max_chars (int): The maximum number of characters per chunk.
+
+    Returns:
+        List[str]: A list of text chunks.
+    """
+    chunks = []
+    current_chunk = ""
+    # Split the text into sentences based on punctuation followed by whitespace
+    sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)
+
+    for sentence in sentences:
+        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
+            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+        else:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
+
+# load vocoder
+def load_vocoder(is_local=False, local_path="", device=device):
+    if is_local:
+        print(f"Load vocos from local path {local_path}")
+        vocos = Vocos.from_hparams(f"{local_path}/config.yaml")
+        state_dict = torch.load(f"{local_path}/pytorch_model.bin", map_location=device)
+        vocos.load_state_dict(state_dict)
+        vocos.eval()
+    else:
+        print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+        vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+    return vocos
+
+
+# load asr pipeline
+
+asr_pipe = None
+
+
+def initialize_asr_pipeline(device=device):
+    global asr_pipe
+    asr_pipe = pipeline(
+        "automatic-speech-recognition",
+        model="openai/whisper-large-v3-turbo",
+        torch_dtype=torch.float16,
+        device=device,
+    )
+
+
+# load model for inference
+
+
+def load_model(model_cls, model_cfg, ckpt_path, vocab_file="", ode_method=ode_method, use_ema=True, device=device):
+    if vocab_file == "":
+        vocab_file = "Emilia_ZH_EN"
+        tokenizer = "pinyin"
+    else:
+        tokenizer = "custom"
+
+    print("\nvocab : ", vocab_file)
+    print("tokenizer : ", tokenizer)
+    print("model : ", ckpt_path, "\n")
+
+    vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer)
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            target_sample_rate=target_sample_rate,
+            n_mel_channels=n_mel_channels,
+            hop_length=hop_length,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)
+
+    return model
+
+
+# preprocess reference audio and text
+
+
+def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print, device=device):
+    show_info("Converting audio...")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        aseg = AudioSegment.from_file(ref_audio_orig)
+
+        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000)
+        non_silent_wave = AudioSegment.silent(duration=0)
+        for non_silent_seg in non_silent_segs:
+            non_silent_wave += non_silent_seg
+        aseg = non_silent_wave
+
+        audio_duration = len(aseg)
+        if audio_duration > 15000:
+            show_info("Audio is over 15s, clipping to only first 15s.")
+            aseg = aseg[:15000]
+        aseg.export(f.name, format="wav")
+        ref_audio = f.name
+
+    if not ref_text.strip():
+        global asr_pipe
+        if asr_pipe is None:
+            initialize_asr_pipeline(device=device)
+        show_info("No reference text provided, transcribing reference audio...")
+        ref_text = asr_pipe(
+            ref_audio,
+            chunk_length_s=30,
+            batch_size=128,
+            generate_kwargs={"task": "transcribe"},
+            return_timestamps=False,
+        )["text"].strip()
+        show_info("Finished transcription")
+    else:
+        show_info("Using custom reference text...")
+
+    # Add the functionality to ensure it ends with ". "
+    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
+    return ref_audio, ref_text
+
+
+# infer process: chunk text -> infer batches [i.e. infer_batch_process()]
+
+
+def infer_process(
+    ref_audio,
+    ref_text,
+    gen_text,
+    model_obj,
+    show_info=print,
+    progress=tqdm,
+    target_rms=target_rms,
+    cross_fade_duration=cross_fade_duration,
+    nfe_step=nfe_step,
+    cfg_strength=cfg_strength,
+    sway_sampling_coef=sway_sampling_coef,
+    speed=speed,
+    fix_duration=fix_duration,
+    device=device,
+):
+    # Split the input text into batches
+    audio, sr = torchaudio.load(ref_audio)
+    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
+    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
+    for i, gen_text in enumerate(gen_text_batches):
+        print(f"gen_text {i}", gen_text)
+
+    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
+    return infer_batch_process(
+        (audio, sr),
+        ref_text,
+        gen_text_batches,
+        model_obj,
+        progress=progress,
+        target_rms=target_rms,
+        cross_fade_duration=cross_fade_duration,
+        nfe_step=nfe_step,
+        cfg_strength=cfg_strength,
+        sway_sampling_coef=sway_sampling_coef,
+        speed=speed,
+        fix_duration=fix_duration,
+        device=device,
+    )
+
+
+# infer batches
+
+
+def infer_batch_process(
+    ref_audio,
+    ref_text,
+    gen_text_batches,
+    model_obj,
+    progress=tqdm,
+    target_rms=0.1,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    cfg_strength=2.0,
+    sway_sampling_coef=-1,
+    speed=1,
+    fix_duration=None,
+    device=None,
+):
+    audio, sr = ref_audio
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    rms = torch.sqrt(torch.mean(torch.square(audio)))
+    if rms < target_rms:
+        audio = audio * target_rms / rms
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+        audio = resampler(audio)
+    audio = audio.to(device)
+
+    generated_waves = []
+    spectrograms = []
+
+    if len(ref_text[-1].encode("utf-8")) == 1:
+        ref_text = ref_text + " "
+    for i, gen_text in enumerate(progress.tqdm(gen_text_batches)):
+        # Prepare the text
+        text_list = [ref_text + gen_text]
+        final_text_list = convert_char_to_pinyin(text_list)
+
+        ref_audio_len = audio.shape[-1] // hop_length
+        if fix_duration is not None:
+            duration = int(fix_duration * target_sample_rate / hop_length)
+        else:
+            # Calculate duration
+            ref_text_len = len(ref_text.encode("utf-8"))
+            gen_text_len = len(gen_text.encode("utf-8"))
+            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
+
+        # inference
+        with torch.inference_mode():
+            generated, _ = model_obj.sample(
+                cond=audio,
+                text=final_text_list,
+                duration=duration,
+                steps=nfe_step,
+                cfg_strength=cfg_strength,
+                sway_sampling_coef=sway_sampling_coef,
+            )
+
+        generated = generated.to(torch.float32)
+        generated = generated[:, ref_audio_len:, :]
+        generated_mel_spec = generated.permute(0, 2, 1)
+        generated_wave = vocos.decode(generated_mel_spec.cpu())
+        if rms < target_rms:
+            generated_wave = generated_wave * rms / target_rms
+
+        # wav -> numpy
+        generated_wave = generated_wave.squeeze().cpu().numpy()
+
+        generated_waves.append(generated_wave)
+        spectrograms.append(generated_mel_spec[0].cpu().numpy())
+
+    # Combine all generated waves with cross-fading
+    if cross_fade_duration <= 0:
+        # Simply concatenate
+        final_wave = np.concatenate(generated_waves)
+    else:
+        final_wave = generated_waves[0]
+        for i in range(1, len(generated_waves)):
+            prev_wave = final_wave
+            next_wave = generated_waves[i]
+
+            # Calculate cross-fade samples, ensuring it does not exceed wave lengths
+            cross_fade_samples = int(cross_fade_duration * target_sample_rate)
+            cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
+
+            if cross_fade_samples <= 0:
+                # No overlap possible, concatenate
+                final_wave = np.concatenate([prev_wave, next_wave])
+                continue
+
+            # Overlapping parts
+            prev_overlap = prev_wave[-cross_fade_samples:]
+            next_overlap = next_wave[:cross_fade_samples]
+
+            # Fade out and fade in
+            fade_out = np.linspace(1, 0, cross_fade_samples)
+            fade_in = np.linspace(0, 1, cross_fade_samples)
+
+            # Cross-faded overlap
+            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
+
+            # Combine
+            new_wave = np.concatenate(
+                [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]]
+            )
+
+            final_wave = new_wave
+
+    # Create a combined spectrogram
+    combined_spectrogram = np.concatenate(spectrograms, axis=1)
+
+    return final_wave, target_sample_rate, combined_spectrogram
+
+
+# remove silence from generated wav
+
+
+def remove_silence_for_generated_wav(filename):
+    aseg = AudioSegment.from_file(filename)
+    non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
+    non_silent_wave = AudioSegment.silent(duration=0)
+    for non_silent_seg in non_silent_segs:
+        non_silent_wave += non_silent_seg
+    aseg = non_silent_wave
+    aseg.export(filename, format="wav")

pyproject.toml

@@ -0,0 +1,62 @@
+[build-system]
+requires = ["setuptools >= 61.0", "setuptools-scm>=8.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "f5-tts"
+version = "0.3.4"
+description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
+readme = "README.md"
+license = {text = "MIT License"}
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+]
+dependencies = [
+    "accelerate>=0.33.0",
+    "bitsandbytes>0.37.0; platform_machine != 'arm64' and platform_system != 'Darwin'",
+    "cached_path",
+    "click",
+    "datasets",
+    "ema_pytorch>=0.5.2",
+    "gradio>=3.45.2",
+    "hydra-core>=1.3.0",
+    "jieba",
+    "librosa",
+    "matplotlib",
+    "numpy<=1.26.4",
+    "pydub",
+    "pypinyin",
+    "safetensors",
+    "soundfile",
+    "tomli",
+    "torch>=2.0.0",
+    "torchaudio>=2.0.0",
+    "torchdiffeq",
+    "tqdm>=4.65.0",
+    "transformers",
+    "transformers_stream_generator",
+    "vocos",
+    "wandb",
+    "x_transformers>=1.31.14",
+]
+
+[project.optional-dependencies]
+eval = [
+    "faster_whisper==0.10.1",
+    "funasr",
+    "jiwer",
+    "modelscope",
+    "zhconv",
+    "zhon",
+]
+
+[project.urls]
+Homepage = "https://github.com/SWivid/F5-TTS"
+
+[project.scripts]
+"f5-tts_infer-cli" = "f5_tts.infer.infer_cli:main"
+"f5-tts_infer-gradio" = "f5_tts.infer.infer_gradio:main"
+"f5-tts_finetune-cli" = "f5_tts.train.finetune_cli:main"
+"f5-tts_finetune-gradio" = "f5_tts.train.finetune_gradio:main"

requirements.txt

@@ -0,0 +1,26 @@
+torch==2.4.0
+torchaudio
+accelerate>=0.33.0
+bitsandbytes>0.37.0
+cached_path
+click
+datasets
+ema_pytorch>=0.5.2
+gradio
+jieba
+librosa
+matplotlib
+numpy<=1.26.4
+pydub
+pypinyin
+safetensors
+soundfile
+tomli
+torchdiffeq
+tqdm>=4.65.0
+transformers
+vocos
+wandb
+x_transformers>=1.31.14
+f5_tts @ git+https://huggingface.co/spaces/mrfakename/E2-F5-TTS
+detoxify @ git+https://github.com/unitaryai/detoxify

requirements_eval.txt

@@ -0,0 +1,5 @@
+faster_whisper
+funasr
+jiwer
+zhconv
+zhon

ruff.toml

@@ -0,0 +1,10 @@
+line-length = 120
+target-version = "py310"
+
+[lint]
+# Only ignore variables with names starting with "_".
+dummy-variable-rgx = "^_.*$"
+
+[lint.isort]
+force-single-line = true
+lines-after-imports = 2

samples/story.toml

@@ -0,0 +1,19 @@
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "samples/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "samples/story.txt"
+remove_silence = true
+output_dir = "samples"
+
+[voices.town]
+ref_audio = "samples/town.flac"
+ref_text = ""
+
+[voices.country]
+ref_audio = "samples/country.flac"
+ref_text = ""
+

samples/story.txt

@@ -0,0 +1 @@
+A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] “My poor dear friend, you live here no better than the ants. Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land.” [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] “Goodbye,” [main] said he, [country] “I’m off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace.”

scripts/count_max_epoch.py

@@ -0,0 +1,33 @@
+"""ADAPTIVE BATCH SIZE"""
+
+print("Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in")
+print("  -> least padding, gather wavs with accumulated frames in a batch\n")
+
+# data
+total_hours = 95282
+mel_hop_length = 256
+mel_sampling_rate = 24000
+
+# target
+wanted_max_updates = 1000000
+
+# train params
+gpus = 8
+frames_per_gpu = 38400  # 8 * 38400 = 307200
+grad_accum = 1
+
+# intermediate
+mini_batch_frames = frames_per_gpu * grad_accum * gpus
+mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+updates_per_epoch = total_hours / mini_batch_hours
+steps_per_epoch = updates_per_epoch * grad_accum
+
+# result
+epochs = wanted_max_updates / updates_per_epoch
+print(f"epochs should be set to: {epochs:.0f} ({epochs/grad_accum:.1f} x gd_acum {grad_accum})")
+print(f"progress_bar should show approx. 0/{updates_per_epoch:.0f} updates")
+print(f"                      or approx. 0/{steps_per_epoch:.0f} steps")
+
+# others
+print(f"total {total_hours:.0f} hours")
+print(f"mini-batch of {mini_batch_frames:.0f} frames, {mini_batch_hours:.2f} hours per mini-batch")

scripts/count_params_gflops.py

@@ -0,0 +1,39 @@
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+from model import M2_TTS, DiT
+
+import torch
+import thop
+
+
+""" ~155M """
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4)
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4, long_skip_connection = True)
+# transformer =     MMDiT(dim = 512, depth = 16, heads = 16, ff_mult = 2)
+
+""" ~335M """
+# FLOPs: 622.1 G, Params: 333.2 M
+# transformer =     UNetT(dim = 1024, depth = 24, heads = 16, ff_mult = 4)
+# FLOPs: 363.4 G, Params: 335.8 M
+transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+
+
+model = M2_TTS(transformer=transformer)
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+duration = 20
+frame_length = int(duration * target_sample_rate / hop_length)
+text_length = 150
+
+flops, params = thop.profile(
+    model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long))
+)
+print(f"FLOPs: {flops / 1e9} G")
+print(f"Params: {params / 1e6} M")