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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
84 lines
3.5 KiB
Markdown
84 lines
3.5 KiB
Markdown
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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# UniSpeech
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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The UniSpeech model was proposed in [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
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Zeng, Xuedong Huang .
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The abstract from the paper is the following:
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*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both
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unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive
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self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture
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information more correlated with phonetic structures and improve the generalization across languages and domains. We
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evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The
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results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
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recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
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testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
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i.e., a relative word error rate reduction of 6% against the previous approach.*
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This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The Authors' code can be
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found [here](https://github.com/microsoft/UniSpeech/tree/main/UniSpeech).
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## Usage tips
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- UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
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use [`Wav2Vec2Processor`] for the feature extraction.
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- UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
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decoded using [`Wav2Vec2CTCTokenizer`].
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## Resources
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- [Audio classification task guide](../tasks/audio_classification)
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- [Automatic speech recognition task guide](../tasks/asr)
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## UniSpeechConfig
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[[autodoc]] UniSpeechConfig
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## UniSpeech specific outputs
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[[autodoc]] models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
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## UniSpeechModel
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[[autodoc]] UniSpeechModel
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- forward
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## UniSpeechForCTC
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[[autodoc]] UniSpeechForCTC
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- forward
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## UniSpeechForSequenceClassification
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[[autodoc]] UniSpeechForSequenceClassification
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- forward
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## UniSpeechForPreTraining
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[[autodoc]] UniSpeechForPreTraining
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- forward
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