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pushing transformer

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  1. README.md +80 -0
  2. asr.ckpt +3 -0
  3. lm.ckpt +3 -0
  4. normalizer.ckpt +3 -0
  5. tokenizer.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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+ - ASR
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+ - CTC
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+ - Attention
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+ - Tranformer
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+ - pytorch
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+ license: "apache-2.0"
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+ datasets:
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+ - librispeech
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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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+ # CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
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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 LibriSpeech (EN) 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). The given ASR model performance are:
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+
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+ | Release | Test clean WER | Test other WER | GPUs |
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+ |:-------------:|:--------------:|:--------------:|:--------:|
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+ | 05-03-21 | 2.90 | 8.51 | 1xV100 16GB |
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+
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+ ## Pipeline description
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+
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+ This ASR system is composed with 3 different but linked blocks:
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+ 1. Tokenizer (unigram) that transforms words into subword units and trained with
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+ the train transcriptions of LibriSpeech.
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+ 2. Neural language model (Transformer LM) trained on the full 10M words dataset.
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+ 3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
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+ N blocks of convolutional neural networks with normalisation and pooling on the
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+ frequency domain. Then, a bidirectional LSTM with projection layers is connected
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+ to a final DNN to obtain the final acoustic representation that is given to
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+ the CTC and attention decoders.
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+
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+ ## Intended uses & limitations
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+
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+ This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model
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+ for the english language. Thanks to the flexibility of SpeechBrain, any of the 3 blocks
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+ detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is
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+ installed.
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+
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+ ## Install SpeechBrain
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+
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+ First of all, please install SpeechBrain with the following command:
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+
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+ ```
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+ pip install \\we hide ! SpeechBrain is still private :p
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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
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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-crdnn-transformerlm-librispeech")
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+ asr_model.transcribe_file("path_to_your_file.wav")
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+
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+ ```
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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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