first commit

docs/source/en/model_doc/speech_to_text.md

@@ -0,0 +1,137 @@
+<!--Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2020-10-11 and contributed to Hugging Face Transformers on 2021-03-10.*
+
+# Speech2Text
+
+
+## Overview
+
+The Speech2Text model was proposed in [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It's a
+transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
+Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
+fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
+transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST:
+[LibriSpeech](http://www.openslr.org/12), [CoVoST 2](https://github.com/facebookresearch/covost), [MuST-C](https://ict.fbk.eu/must-c/).
+
+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).
+
+## Inference
+
+Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech
+signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The
+`generate()` method can be used for inference.
+
+The [`Speech2TextFeatureExtractor`] class is responsible for extracting the log-mel filter-bank
+features. The [`Speech2TextProcessor`] wraps [`Speech2TextFeatureExtractor`] and
+[`Speech2TextTokenizer`] into a single instance to both extract the input features and decode the
+predicted token ids.
+
+The feature extractor depends on `torchaudio` and the tokenizer depends on `sentencepiece` so be sure to
+install those packages before running the examples. You could either install those as extra speech dependencies with
+`pip install transformers"[speech, sentencepiece]"` or install the packages separately 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
+be installed as follows: `apt install libsndfile1-dev`
+
+- ASR and Speech Translation
+
+```python
+from datasets import load_dataset
+
+from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
+
+
+model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr", device_map="auto")
+processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
+
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+
+inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt").to(model.device)
+generated_ids = model.generate(inputs["input_features"], attention_mask=inputs["attention_mask"])
+
+transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
+transcription
+['mister quilter is the apostle of the middle classes and we are glad to welcome his gospel']
+```
+
+- Multilingual speech translation
+
+  For multilingual speech translation models, `eos_token_id` is used as the `decoder_start_token_id` and
+  the target language id is forced as the first generated token. To force the target language id as the first
+  generated token, pass the `forced_bos_token_id` parameter to the `generate()` method. The following
+  example shows how to translate English speech to French text using the *facebook/s2t-medium-mustc-multilingual-st*
+  checkpoint.
+
+```python
+from datasets import load_dataset
+
+from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
+
+
+model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st", device_map="auto")
+processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+
+inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    inputs["input_features"],
+    attention_mask=inputs["attention_mask"],
+    forced_bos_token_id=processor.tokenizer.lang_code_to_id["fr"],
+)
+
+translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
+translation
+["(Vidéo) Si M. Kilder est l'apossible des classes moyennes, et nous sommes heureux d'être accueillis dans son évangile."]
+```
+
+See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for Speech2Text checkpoints.
+
+## Speech2TextConfig
+
+[[autodoc]] Speech2TextConfig
+
+## Speech2TextTokenizer
+
+[[autodoc]] Speech2TextTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## Speech2TextFeatureExtractor
+
+[[autodoc]] Speech2TextFeatureExtractor
+    - __call__
+
+## Speech2TextProcessor
+
+[[autodoc]] Speech2TextProcessor
+    - __call__
+    - from_pretrained
+    - save_pretrained
+    - batch_decode
+    - decode
+
+## Speech2TextModel
+
+[[autodoc]] Speech2TextModel
+    - forward
+
+## Speech2TextForConditionalGeneration
+
+[[autodoc]] Speech2TextForConditionalGeneration
+    - forward