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  1. README.md +108 -0
  2. asr.ckpt +3 -0
  3. config.json +68 -0
  4. preprocessor_config.json +8 -0
  5. tokenizer.ckpt +3 -0
  6. wav2vec2.ckpt +3 -0
README.md ADDED
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+ ---
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+ language: "en"
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+ thumbnail:
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+ tags:
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+ - automatic-speech-recognition
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+ - CTC
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+ - Attention
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+ - pytorch
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+ - speechbrain
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+ - Transformer
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+ license: "apache-2.0"
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+ datasets:
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+ - commonvoice
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+ metrics:
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+ - wer
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+ - cer
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+ ---
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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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+
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+ # wav2vec 2.0 with CTC/Attention trained on CommonVoice English (No LM)
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+
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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 CommonVoice (English Language) 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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+
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+ The performance of the model is the following:
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+
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+ | Release | Test WER | GPUs |
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+ |:-------------:|:--------------:|:--------------:| :--------:|
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+ | 29-04-21 | 15.69 | 2xV100 32GB |
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+
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+ ## Pipeline description
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+
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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 and trained with
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+ the train transcriptions (train.tsv) of CommonVoice (EN).
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+ - Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([wav2vec2-lv60-large](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on CommonVoice En.
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+ The obtained final acoustic representation is given to the CTC and attention decoders.
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+
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+
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+ ## Install SpeechBrain
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+
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+ First of all, please install tranformers and SpeechBrain with the following command:
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+
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+ ```
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+ pip install speechbrain transformers
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+ ```
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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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+
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+ ### Transcribing your own audio files (in French)
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+
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+ ```python
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+ from speechbrain.pretrained import EncoderDecoderASR
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+
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+ asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-fr", savedir="pretrained_models/asr-wav2vec2-commonvoice-en")
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+ asr_model.transcribe_file("example-en.wav")
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+
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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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+
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+ ### Training
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+ The model was trained with SpeechBrain.
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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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+
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+ 3. Run Training:
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+ ```bash
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+ cd recipes/CommonVoice/ASR/seq2seq
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+ python train.py hparams/train_fr_with_wav2vec.yaml --data_folder=your_data_folder
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+ ```
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+
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+ You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing).
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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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+
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+ #### Referencing SpeechBrain
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+
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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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+
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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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config.json ADDED
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+ {
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+ "apply_spec_augment": true,
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+ "architectures": [
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+ "Wav2Vec2Model"
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+ "ctc_loss_reduction": "sum",
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+ "ctc_zero_infinity": false,
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+ "do_stable_layer_norm": true,
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+ "eos_token_id": 2,
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+ "feat_extract_activation": "gelu",
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+ "layer_norm_eps": 1e-05,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_prob": 0.05,
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.4.0.dev0",
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+ "vocab_size": 32
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+ }
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