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Speech2Text
-----------------------------------------------------------------------------------------------------------------------

Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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
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
:obj:`generate()` method can be used for inference.

The :class:`~transformers.Speech2TextFeatureExtractor` class is responsible for extracting the log-mel filter-bank
features. The :class:`~transformers.Speech2TextProcessor` wraps :class:`~transformers.Speech2TextFeatureExtractor` and
:class:`~transformers.Speech2TextTokenizer` into a single instance to both extract the input features and decode the
predicted token ids.

The feature extractor depends on :obj:`torchaudio` and the tokenizer depends on :obj:`sentencepiece` so be sure to
install those packages before running the examples. You could either install those as extra speech dependancies with
``pip install transformers"[speech, sentencepiece]"`` or install the packages seperatly 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

.. code-block::

        >>> import torch
        >>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
        >>> from datasets import load_dataset
        >>> import soundfile as sf

        >>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
        >>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")

        >>> def map_to_array(batch):
        ...     speech, _ = sf.read(batch["file"])
        ...     batch["speech"] = speech
        ...     return batch

        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.map(map_to_array)

        >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
        >>> generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])

        >>> transcription = processor.batch_decode(generated_ids)


- Multilingual speech translation

    For multilingual speech translation models, :obj:`eos_token_id` is used as the :obj:`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 :obj:`forced_bos_token_id` parameter to the :obj:`generate()` method. The following
    example shows how to transate English speech to French text using the `facebook/s2t-medium-mustc-multilingual-st`
    checkpoint.

.. code-block::

        >>> import torch
        >>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
        >>> from datasets import load_dataset
        >>> import soundfile as sf

        >>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
        >>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")

        >>> def map_to_array(batch):
        ...     speech, _ = sf.read(batch["file"])
        ...     batch["speech"] = speech
        ...     return batch

        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.map(map_to_array)

        >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
        >>> generated_ids = model.generate(input_ids=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)


See the `model hub <https://huggingface.co/models?filter=speech_to_text>`__ to look for Speech2Text checkpoints.


Speech2TextConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextConfig
    :members:


Speech2TextTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
        create_token_type_ids_from_sequences, save_vocabulary


Speech2TextFeatureExtractor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextFeatureExtractor
    :members: __call__


Speech2TextProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextProcessor
    :members: __call__, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor


Speech2TextModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextModel
    :members: forward


Speech2TextForConditionalGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.Speech2TextForConditionalGeneration
    :members: forward
