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README.md ADDED
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+ ---
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+ language: en
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+ datasets:
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+ - librispeech_asr
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ license: MIT
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+ ---
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+
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+
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+ # S2T-LARGE-LIBRISPEECH-ASR
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+
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+ `s2t-large-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
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+ The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
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+ [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
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+
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+
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+ ## Model description
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+
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+ S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard
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+ autoregressive cross-entropy loss and generates the transcripts autoregressively.
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+
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+ ## Intended uses & limitations
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+
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+ This model can be used for end-to-end speech recognition (ASR).
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+ See the [model hub](https://huggingface.co/models?filter=speech_to_text_transformer) to look for other S2T checkpoints.
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+
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+
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+ ### How to use
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+
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+ As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
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+ transcripts by passing the speech features to the model.
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+
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+ *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
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+ filter bank features. Make sure to install the `torchaudio` package before running this example.*
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+
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+ To install `torchaudio` run `pip install torchaudio`
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+
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+
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+ ```python
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+ import torch
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+ from transformers import Speech2TextProcessor, Speech2TextTransformerForConditionalGeneration
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+ from datasets import load_dataset
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+ import soundfile as sf
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+
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+ model = Speech2TextTransformerForConditionalGeneration.from_pretrained("facebook/s2t-large-librispeech-asr")
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+ processor = Speech2Textprocessor.from_pretrained("facebook/s2t-large-librispeech-asr")
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+
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+ def map_to_array(batch):
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+ speech, _ = sf.read(batch["file"])
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+ batch["speech"] = speech
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+ return batch
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+
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+ ds = load_dataset(
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+ "patrickvonplaten/librispeech_asr_dummy",
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+ "clean",
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+ split="validation"
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+ )
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+ ds = ds.map(map_to_array)
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+
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+ input_features = processor(
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+ ds["speech"][0],
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+ sampling_rate=16_000,
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+ return_tensors="pt"
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+ ).input_features # Batch size 1
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+ generated_ids = model.generate(input_ids=input_features)
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+
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+ transcription = processor.batch_decode(generated_ids)
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+ ```
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+
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+ #### Evaluation on LibriSpeech Test
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+
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+ The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
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+ *"clean"* and *"other"* test dataset.
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+
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+ ```python
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+ from datasets import load_dataset, load_metric
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+ from transformers import Speech2TextTransformerForConditionalGeneration, Speech2TextProcessor
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+ import soundfile as sf
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+
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+ librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset
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+ wer = load_metric("wer")
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+
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+ model = Speech2TextTransformerForConditionalGeneration.from_pretrained("facebook/s2t-large-librispeech-asr").to("cuda")
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+ processor = Speech2TextProcessor.from_pretrained("facebook/s2t-large-librispeech-asr", do_upper_case=True)
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+
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+ def map_to_array(batch):
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+ speech, _ = sf.read(batch["file"])
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+ batch["speech"] = speech
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+ return batch
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+
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+ librispeech_eval = librispeech_eval.map(map_to_array)
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+
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+ def map_to_pred(batch):
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+ features = processor(batch["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
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+ input_features = features.input_features.to("cuda")
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+ attention_mask = features.attention_mask.to("cuda")
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+
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+ gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
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+ batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)
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+ return batch
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+
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+ result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])
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+
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+ print("WER:", wer(predictions=result["transcription"], references=result["text"]))
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+ ```
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+
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+ *Result (WER)*:
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+
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+ | "clean" | "other" |
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+ |:-------:|:-------:|
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+ | 3.3 | 7.5 |
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+
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+
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+
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+ ## Training data
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+
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+ The S2T-LARGE-LIBRISPEECH-ASR is trained on [LibriSpeech ASR Corpus](https://www.openslr.org/12), a dataset consisting of
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+ approximately 1000 hours of 16kHz read English speech.
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+
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
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+ WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
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+ is applied to each example.
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+
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+ The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 10,000.
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+
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+
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+ ### Training
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+
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+ The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
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+ The encoder receives speech features, and the decoder generates the transcripts autoregressively.
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @inproceedings{wang2020fairseqs2t,
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+ title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
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+ author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
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+ booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
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+ year = {2020},
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+ }
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+
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+ ```
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