# Copyright (c) 2025 MediaTek Reserch Inc (authors: Chan-Jan Hsu) # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Liu Yue) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR)) import argparse import gradio as gr import numpy as np import torch torch.set_num_threads(1) import torchaudio import random import librosa from transformers import pipeline import subprocess from scipy.signal import resample import logging logging.getLogger('matplotlib').setLevel(logging.WARNING) from cosyvoice.cli.cosyvoice import CosyVoice from cosyvoice.utils.file_utils import load_wav, speed_change #logging.basicConfig(level=logging.DEBUG, # format='%(asctime)s %(levelname)s %(message)s') def generate_seed(): seed = random.randint(1, 100000000) return { "__type__": "update", "value": seed } def set_all_random_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) max_val = 0.8 def postprocess(speech, top_db=60, hop_length=220, win_length=440): speech, _ = librosa.effects.trim( speech, top_db=top_db, frame_length=win_length, hop_length=hop_length ) if speech.abs().max() > max_val: speech = speech / speech.abs().max() * max_val speech = torch.concat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1) return speech def generate_audio(tts_text, prompt_text, prompt_wav_upload, prompt_wav_record, seed, select_which): if select_which == "上傳檔案" and prompt_wav_upload is not None: prompt_wav = prompt_wav_upload elif select_which == "麥克風" and prompt_wav_record is not None: prompt_wav = prompt_wav_record else: prompt_wav = None # if instruct mode, please make sure that model is iic/CosyVoice-300M-Instruct and not cross_lingual mode prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr)) set_all_random_seed(seed) output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k) speed_factor = 1 if speed_factor != 1.0: #try: #audio_data, sample_rate = speed_change(output["tts_speech"], target_sr, str(speed_factor)) #audio_data = audio_data.numpy().flatten() new_length = int(len(output['tts_speech']) / speed_factor) audio_data = resample(output['tts_speech'], new_length) # except Exception as e: # print(f"Failed to change speed of audio: \n{e}") else: audio_data = output['tts_speech'].numpy().flatten() return (target_sr, audio_data) def generate_text(prompt_wav_upload, prompt_wav_record, select_which): # Determine which input to use based on the selection in select_which if select_which == "上傳檔案" and prompt_wav_upload is not None: prompt_wav = prompt_wav_upload LAST_UPLOADED = "upload" elif select_which == "麥克風" and prompt_wav_record is not None: prompt_wav = prompt_wav_record LAST_UPLOADED = "record" else: prompt_wav = None LAST_UPLOADED = None print(select_which) # Process with ASR pipeline if prompt_wav: results = asr_pipeline(prompt_wav) return results['text'] return "No valid input detected." # LAST_UPLOADED = "" # def switch_selected(select_which): # # Check the file type (assuming WAV file) # if select_which == "上傳檔案" and prompt_wav_upload is not None: # prompt_wav = prompt_wav_upload # LAST_UPLOADED = "upload" # elif select_which == "麥克風" and prompt_wav_record is not None: # prompt_wav = prompt_wav_record # return "麥克風" def demo_get_audio(tts_text): sample_wav = 'sample.wav' speech, sample_rate = torchaudio.load(sample_wav) return sample_rate, speech def main(): with gr.Blocks(title="BreezyVoice 語音合成系統", theme="default") as demo: # Title and About section at the top gr.Markdown("# BreezyVoice 語音合成系統") gr.Markdown( """### 僅需5秒語音樣本，就可輸出擬真人聲。""" ) with gr.Row(): gr.Image(value="https://huggingface.co/spaces/Splend1dchan/BreezyVoice-Playground/resolve/main/flowchart.png", interactive=False, scale=3) gr.Markdown( """#### 此沙盒使用 Huggingface CPU，請預期大於200 秒的推理時間，您可以考慮以下方法加速： 1. **強烈建議**複製這個 Space（Duplicate this space），以分散流量！ 2. 複製至本地GPU執行（請參考[指南](https://huggingface.co/docs/hub/en/spaces-overview)）或使用[kaggle](https://www.kaggle.com/code/a24998667/breezyvoice-playground) 3. 複製至本地CPU執行（請參考[指南](https://huggingface.co/docs/hub/en/spaces-overview)） 為了加快推理速度，g2pw注音標註並未被啟動。 免責聲明：此沙盒在一次性容器地端執行，關閉後檔案將遭到刪除。此沙盒不屬於聯發創新基地，聯發創新基地無法獲得任何使用者輸入。""" ) # All content arranged in a single column with gr.Column(): # Configuration Section # Grouping prompt audio inputs and auto speech recognition in one block using Markdown gr.Markdown("### 步驟 1. 音訊樣本輸入 & 音訊樣本文本輸入") gr.Markdown("選擇prompt音訊檔案或錄製prompt音訊 (5~15秒)，並手動校對自動產生的音訊樣本文本。") prompt_wav_upload = gr.Audio( sources='upload', type='filepath', label='選擇prompt音訊檔案（確保取樣率不低於16khz）' ) prompt_wav_record = gr.Audio( sources='microphone', type='filepath', label='錄製prompt音訊檔案' ) with gr.Blocks(): select_which = gr.Radio(["上傳檔案", "麥克風"], label="音訊來源", interactive=True ) with gr.Blocks(): prompt_text = gr.Textbox( label="音訊樣本文本輸入(此欄位應與音檔內容完全相同)", lines=2, placeholder="音訊樣本文本" ) # Automatic speech recognition when either prompt audio input changes def a(X): return "上傳檔案" prompt_wav_upload.change( fn=a,#lambda file: "上傳檔案", inputs=[prompt_wav_upload], outputs=select_which ) prompt_wav_record.change( fn=lambda recording: "麥克風", inputs=[prompt_wav_record], outputs=select_which ) select_which.change( fn=generate_text, inputs=[prompt_wav_upload, prompt_wav_record, select_which], outputs=prompt_text ) # select_which.change( # fn=switch_selected, # inputs=[select_which], # outputs= None # ) # Input Section: Synthesis Text gr.Markdown("### 步驟 2.合成文本輸入") tts_text = gr.Textbox( label="輸入想要合成的文本", lines=2, placeholder="請輸入想要合成的文本...", value="你好，歡迎光臨" ) # Output Section gr.Markdown("### 步驟 3. 合成音訊") # Generation button for audio synthesis (triggered manually) with gr.Accordion("進階設定", open=False): seed = gr.Number(value=0, label="隨機推理種子") #seed_button = gr.Button("隨機") seed_button = gr.Button(value="\U0001F3B2生成隨機推理種子\U0001F3B2") speed_factor = 1 # speed_factor = gr.Slider( # minimum=0.25, # maximum=4, # step=0.05, # label="語速", # value=1.0, # interactive=True # ) generate_button = gr.Button("生成音訊") audio_output = gr.Audio(label="合成音訊") # Set up callbacks for seed generation and audio synthesis seed_button.click(fn=generate_seed, inputs=[], outputs=seed) generate_button.click( fn=generate_audio, inputs=[tts_text, prompt_text, prompt_wav_upload, prompt_wav_record, seed, select_which], outputs=audio_output ) demo.queue(max_size=10, default_concurrency_limit=1) demo.launch() if __name__ == '__main__': cosyvoice = CosyVoice('Splend1dchan/BreezyVoice') asr_pipeline = pipeline( "automatic-speech-recognition", model="openai/whisper-tiny", tokenizer="openai/whisper-tiny", device=0 # Use GPU (if available); set to -1 for CPU ) sft_spk = cosyvoice.list_avaliable_spks() prompt_sr, target_sr = 16000, 22050 default_data = np.zeros(target_sr) main()