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* added cbs to notebooks, made copy-paste error fix in generation_utils * initial push for mctc model * mctc feature extractor done * added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly. * added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly. * passing attention, now struggling to figure out how attention masks make sense here * works when excluding attention masks. ask later how one would integrate attention maskshere * bizarre configuration error (model prefix comes first in config dict json and messes up the order) * all passing but bizzarre config dict ordering issue when to_dict * passing all major tests * feature extraction, processor, tokenizer added & tests passing * style & consistency & other logistical fixes * copy paste fix * model after feature extraction working * commiting final feature extraction results; need to fix normalization * feature extraction passing tests; probably should add tests on the specific flashlight-copied functions? * delete print ; format code a bit * fixing tests * passing major tests * fixing styles * completed tokenization test with real example; not sure if these values are entirely correct. * last test fixes from local * reverting accidentally included custom setup configs * remove load tf weights; fix config error * testing couldnt import featureextractor * fix docs * fix docs * resolving comments * style fixes * style fixes * Update to MCTCConv1dSubSampler Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * relposemb fixes * conv1d name issue; expecting config fail with paraentheses * fix config issue * fix config issue * fix config issue * change everything to MCTCT * fixing naming change errors * archive list * copyrights and docs * copyrights and docs * copyrights and docs * merge resolution * move tests, fix to changed optionaldependency structure * test directories changed * fixing tests * how to avoid tf tests? * how to avoid tf tests? * tests passing locally * allow mctctprocessor imported any env * allow mctctprocessor imported any env * fixed second round of feedback, need to fix docs * doc changes not being applied * all fixed * style fix * feedback fixes * fix copies and feature extraction style fix * Update tests/models/visual_bert/test_modeling_visual_bert.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * copy paste huggingface:main visual bert * added eof newline to visual bert; all tests are passing otherwise * fix slow tests by adding attention mask * change model id to speechbrain * make fix-copies * fix readme unwanted deletes * fixing readmes, make fix-copies * consistent M-CTC-T naming * Update src/transformers/models/mctct/__init__.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * all fixed but variable naming * adjust double quotes * fixed variable names * copyright and mr quilter * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * correct slow tests * make fix-copies * Update src/transformers/models/mctct/configuration_mctct.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/mctct/configuration_mctct.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * m-ctc-t not mctct Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
63 lines
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63 lines
2.7 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# M-CTC-T
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## Overview
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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.
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The abstract from the paper is the following:
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*Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual
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speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech
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recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even
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with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised
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learning on a target language, generate pseudo-labels for that language, and train a final model using
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pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled
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Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better
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performance for many languages that also transfers well to LibriSpeech.*
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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).
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## MCTCTConfig
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[[autodoc]] MCTCTConfig
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## MCTCTFeatureExtractor
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[[autodoc]] MCTCTFeatureExtractor
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- __call__
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## MCTCTProcessor
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[[autodoc]] MCTCTProcessor
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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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## MCTCTModel
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[[autodoc]] MCTCTModel
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- forward
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## MCTCTForCTC
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[[autodoc]] MCTCTForCTC
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- forward
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