# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
<!--Copyright 2022 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.

-->

# M-CTC-T

## Overview

The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.

The abstract from the paper is the following:

*Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual
speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech
recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even
with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised
learning on a target language, generate pseudo-labels for that language, and train a final model using
pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled
Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better
performance for many languages that also transfers well to LibriSpeech.*



This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl).

## Documentation resources

- [Automatic speech recognition task guide](../tasks/asr)

Tips:

- The PyTorch version of this model is only available in torch 1.9 and higher.

## MCTCTConfig

[[autodoc]] MCTCTConfig

## MCTCTFeatureExtractor

[[autodoc]] MCTCTFeatureExtractor
    - __call__

## MCTCTProcessor

[[autodoc]] MCTCTProcessor
    - __call__
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode


## MCTCTModel

[[autodoc]] MCTCTModel
    - forward

## MCTCTForCTC

[[autodoc]] MCTCTForCTC
    - forward

<!--Copyright 2022 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.

-->

# M-CTC-T

<Tip warning={true}>

This model is in maintenance mode only, so we won't accept any new PRs changing its code.

If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.

</Tip>

## Overview

The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.

The abstract from the paper is the following:

*Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual
speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech
recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even
with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised
learning on a target language, generate pseudo-labels for that language, and train a final model using
pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled
Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better
performance for many languages that also transfers well to LibriSpeech.*



This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl).

## Documentation resources

- [Automatic speech recognition task guide](../tasks/asr)

Tips:

- The PyTorch version of this model is only available in torch 1.9 and higher.

## MCTCTConfig

[[autodoc]] MCTCTConfig

## MCTCTFeatureExtractor

[[autodoc]] MCTCTFeatureExtractor
    - __call__

## MCTCTProcessor

[[autodoc]] MCTCTProcessor
    - __call__
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode


## MCTCTModel

[[autodoc]] MCTCTModel
    - forward

## MCTCTForCTC

[[autodoc]] MCTCTForCTC
    - forward
