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--- |
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language: |
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- en |
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thumbnail: null |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- CTC |
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- pytorch |
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- speechbrain |
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license: apache-2.0 |
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datasets: |
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- switchboard |
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metrics: |
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- wer |
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- ser |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# wav2vec 2.0 with CTC/Attention trained on Switchboard (No LM) |
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This repository provides all the necessary tools to perform automatic speech |
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recognition from an end-to-end system pretrained on the Switchboard corpus within |
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SpeechBrain. For a better experience, we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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The performance of the model is the following: |
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| Release | Swbd SER | Callhome SER | Eval2000 SER | Swbd WER | Callhome WER | Eval2000 WER | GPUs | |
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|:--------:|:--------:|:------------:|:------------:|:--------:|:------------:|:------------:|:-----------:| |
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| 17-09-22 | 48.60 | 55.76 | 52.96 | 8 .76 | 14.67 | 11.78 | 4xA100 40GB | |
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## Pipeline description |
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This ASR system is composed of 2 different but linked blocks: |
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- Tokenizer (unigram) that transforms words into subword units trained on the Switchboard training transcripts and the Fisher corpus. |
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- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on Switchboard |
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The obtained final acoustic representation is given to the CTC greedy decoder. |
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The system is trained with recordings sampled at 16kHz (single channel). |
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The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. |
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## Install SpeechBrain |
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First of all, please install tranformers and SpeechBrain with the following command: |
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``` |
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pip install speechbrain transformers |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Transcribing your own audio files |
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```python |
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from speechbrain.pretrained import EncoderASR |
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asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-switchboard", savedir="pretrained_models/asr-wav2vec2-switchboard") |
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asr_model.transcribe_file('path/to/audiofile') |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Training |
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The model was trained with SpeechBrain (Commit hash: '70904d0'). |
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To train it from scratch follow these steps: |
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1. Clone SpeechBrain: |
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```bash |
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git clone https://github.com/speechbrain/speechbrain/ |
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``` |
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2. Install it: |
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```bash |
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cd speechbrain |
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pip install -r requirements.txt |
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pip install -e . |
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``` |
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3. Run Training: |
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```bash |
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cd recipes/Switchboard/ASR/CTC/ |
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python train_with_wav2vec.py hparams/train_with_wav2vec.yaml --data_folder=your_data_folder |
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``` |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### About SpeechBrain |
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SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
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