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--- |
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license: cc |
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datasets: |
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- speechlab/SPRING_INX_R1 |
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tags: |
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- ASR |
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- speech-recognition |
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--- |
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# Fairseq Inference Setup and Usage |
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This repository provides a streamlined setup and guide for performing inference with Fairseq models, tailored for automatic speech recognition. |
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## Table of Contents |
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1. [Setup Instructions](#setup-instructions) |
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2. [Download Required Models](#download-required-models) |
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3. [Running Inference](#running-inference) |
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4. [Getting Transcripts](#getting-transcripts) |
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--- |
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### Setup Instructions |
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To set up the environment and install necessary dependencies for Fairseq inference, follow these steps. |
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#### 1. Create and Activate a Virtual Environment |
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Choose between Python's `venv` or Conda for environment management. |
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Using `venv`: |
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```bash |
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python3.8 -m venv lm_env # use python3.8 or adjust for your preferred version |
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source lm_env/bin/activate |
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``` |
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Using Conda: |
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```bash |
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conda create -n fairseq_inference python==3.8.10 |
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conda activate fairseq_inference |
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``` |
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#### 2. Install PyTorch and CUDA |
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Install the appropriate version of PyTorch and CUDA for your setup: |
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```bash |
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pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html |
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``` |
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If using Python 3.10.15 and CUDA 12.4: |
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```bash |
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pip install torch+cu124 torchvision+cu124 torchaudio+cu124 -f https://download.pytorch.org/whl/cu124/torch_stable.html |
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``` |
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#### 3. Install Additional Packages |
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```bash |
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pip install wheel soundfile editdistance pyarrow tensorboard tensorboardX |
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``` |
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#### 4. Clone the Fairseq Inference Repository |
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```bash |
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git clone https://github.com/Speech-Lab-IITM/Fairseq-Inference.git |
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cd Fairseq-Inference/fairseq-0.12.2 |
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pip install --editable ./ |
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python setup.py build develop |
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``` |
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--- |
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### Download Required Models |
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Download the necessary models for your ASR tasks. Place them in the appropriate directory (`model_path`). |
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### Running Inference |
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Once setup is complete and models are downloaded, use the following command to run inference: |
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```bash |
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python3 infer.py model_path audio_path |
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``` |
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This script takes in the model directory and an audio file to generate a transcription. |
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### Getting Transcripts |
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After running the inference script, you will receive the transcript for the provided audio file in the output. |
