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144 lines
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144 lines
5.9 KiB
Plaintext
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# Speech2Text
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## Overview
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The Speech2Text model was proposed in [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It's a
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transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
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Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
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fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
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transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST:
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[LibriSpeech](http://www.openslr.org/12), [CoVoST 2](https://github.com/facebookresearch/covost), [MuST-C](https://ict.fbk.eu/must-c/).
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This model was contributed by [valhalla](https://huggingface.co/valhalla). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text).
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## Inference
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Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech
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signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The
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`generate()` method can be used for inference.
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The [`Speech2TextFeatureExtractor`] class is responsible for extracting the log-mel filter-bank
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features. The [`Speech2TextProcessor`] wraps [`Speech2TextFeatureExtractor`] and
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[`Speech2TextTokenizer`] into a single instance to both extract the input features and decode the
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predicted token ids.
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The feature extractor depends on `torchaudio` and the tokenizer depends on `sentencepiece` so be sure to
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install those packages before running the examples. You could either install those as extra speech dependencies with
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`pip install transformers"[speech, sentencepiece]"` or install the packages seperately with `pip install torchaudio sentencepiece`. Also `torchaudio` requires the development version of the [libsndfile](http://www.mega-nerd.com/libsndfile/) package which can be installed via a system package manager. On Ubuntu it can
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be installed as follows: `apt install libsndfile1-dev`
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- ASR and Speech Translation
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```python
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>>> import torch
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>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
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>>> from datasets import load_dataset
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>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
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>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt")
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>>> generated_ids = model.generate(inputs["input_features"], attention_mask=inputs["attention_mask"])
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>>> transcription = processor.batch_decode(generated_ids)
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>>> transcription
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['mister quilter is the apostle of the middle classes and we are glad to welcome his gospel']
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```
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- Multilingual speech translation
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For multilingual speech translation models, `eos_token_id` is used as the `decoder_start_token_id` and
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the target language id is forced as the first generated token. To force the target language id as the first
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generated token, pass the `forced_bos_token_id` parameter to the `generate()` method. The following
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example shows how to transate English speech to French text using the *facebook/s2t-medium-mustc-multilingual-st*
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checkpoint.
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```python
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>>> import torch
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>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
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>>> from datasets import load_dataset
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>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
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>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt")
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>>> generated_ids = model.generate(
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... inputs["input_features"],
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... attention_mask=inputs["attention_mask"],
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... forced_bos_token_id=processor.tokenizer.lang_code_to_id["fr"],
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... )
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>>> translation = processor.batch_decode(generated_ids)
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>>> translation
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["<lang:fr> (Vidéo) Si M. Kilder est l'apossible des classes moyennes, et nous sommes heureux d'être accueillis dans son évangile."]
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```
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See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for Speech2Text checkpoints.
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## Speech2TextConfig
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[[autodoc]] Speech2TextConfig
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## Speech2TextTokenizer
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[[autodoc]] Speech2TextTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## Speech2TextFeatureExtractor
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[[autodoc]] Speech2TextFeatureExtractor
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- __call__
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## Speech2TextProcessor
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[[autodoc]] Speech2TextProcessor
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- __call__
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- from_pretrained
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- save_pretrained
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- batch_decode
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- decode
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- as_target_processor
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## Speech2TextModel
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[[autodoc]] Speech2TextModel
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- forward
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## Speech2TextForConditionalGeneration
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[[autodoc]] Speech2TextForConditionalGeneration
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- forward
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## TFSpeech2TextModel
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[[autodoc]] TFSpeech2TextModel
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- call
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## TFSpeech2TextForConditionalGeneration
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[[autodoc]] TFSpeech2TextForConditionalGeneration
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- call
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