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Wav2vec 2.0: Learning the structure of speech from raw audio

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  • We are releasing pretrained units and code for wav2vec 2.0, the successor to wav2vec. This contemporary model learns traditional speech units dilapidated to possess a self-supervised assignment. The model is educated to predict the honest speech unit for masked parts of the audio, whereas on the equivalent time discovering out what the speech units desires to be.

  • With factual 10 minutes of transcribed speech and 53K hours of unlabeled speech, wav2vec 2.0 enables speech recognition units at a be aware error rate (WER) of 8.6 p.c on noisy speech and 5.2 p.c on trim speech on the commonplace LibriSpeech benchmark.

  • This opens the door for speech recognition units in many more languages, dialects, and domains that previously required some distance more transcribed audio recordsdata to manufacture acceptable accuracy.

  • We also developed a injurious-lingual manner, dubbed XLSR, that might perhaps be taught speech units neatly-liked to loads of languages. This kind helps when now we enjoy even cramped quantities of unlabeled speech, since languages for which now we enjoy shrimp recordsdata can enjoy the serve of languages for which more recordsdata is without delay available.

There are hundreds of languages spoken across the sector, many with loads of moderately about a dialects, which items an big bother for building high-effective speech recognition expertise. It’s simply no longer feasible to receive sources for every dialect and each language across the many that you might issue of domains (read speech, cell phone speech, etc.). Our contemporary model, wav2vec 2.0, makes exercise of self-supervision to push the boundaries by discovering out from unlabeled practicing recordsdata to enable speech recognition programs for many more languages, dialects, and domains. With factual one hour of labeled practicing recordsdata, wav2vec 2.0 outperforms the old hiss of the artwork on the 100-hour subset of the LibriSpeech benchmark — the exercise of 100 cases less labeled recordsdata.

Similar to the Bidirectional Encoder Representations from Transformers (BERT), our model is educated by predicting speech units for masked parts of the audio. A major distinction is that speech audio is a continuous price that captures many aspects of the recording without a determined segmentation into phrases or moderately about a units. Wav2vec 2.0 tackles this bother by discovering out traditional units which can be 25ms prolonged to enable discovering out of high-stage contextualized representations. These units are then dilapidated to scream many thoroughly different speech audio recordings and create wav2vec more sturdy. This enables us to effect speech recognition programs that might perhaps outperform the most clear-cut semisupervised strategies, even with 100x less labeled practicing recordsdata.

Wav2vec 2.0 is segment of our imaginative and prescient for machine discovering out units that depend less on labeled recordsdata, attributable to self-supervised discovering out. Self-supervision has helped us near listing classification, video knowing, and our recount material knowing programs. We hope that the algorithm will enable improved speech expertise for many more languages, dialects, and domains, and consequence in enhancements for existing programs.

Learning discrete latent speech units

Dilapidated speech recognition units are primarily educated on annotated speech audio with transcriptions. Gorgeous programs require big quantities of annotated recordsdata, which is fully readily available for a cramped selection of languages. Self-supervision supplies a potential to leverage unannotated recordsdata to effect greater programs.

Other self-supervised approaches for speech strive to reconstruct the audio price, which requires the model to gain every aspect of the speech, collectively with recording ambiance, channel noise, and speaker traits. One other neatly-liked manner is to practice the model by asking it to predict what the speaker acknowledged subsequent by contrasting loads of alternatives.

Our manner learns a station of speech units, which are shorter than phonemes, to scream the speech audio sequence. Since this station is finite, the model can no longer verbalize all variations, similar to background noise. As an replace, the units wait on the model to focal point on the largest components to scream the speech audio. In our experiments, we web that this works greater than replace approaches on the LibriSpeech benchmark.

The model first processes the uncooked waveform of the speech audio with a multilayer convolutional neural network to receive latent audio representations of 25ms every. These representations are then fed valid into a quantizer moreover to a transformer. The quantizer chooses a speech unit for the latent audio illustration from an stock of learned units. About half the audio representations are masked before being fed into the transformer. The transformer adds recordsdata from the total audio sequence. At closing, the output of the transformer is dilapidated to clear up a contrastive assignment. This assignment requires the model to call the honest quantized speech units for the masked positions.

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With injurious-lingual practicing, wav2vec 2.0 learns speech units which can be dilapidated in a few languages.

Unsuitable-lingual practicing

For some languages, even unannotated recordsdata is limited. To address this bother, we explore the postulate of injurious-lingual practicing. The premise is to pretrain a single model on a few languages on the equivalent time, which ends up in representations which can be greater than practicing on a single language. This has labored significantly effectively for pure language processing with XLM-R. Performance for low-useful resource languages can strengthen vastly with this design, since they’ve the serve of related languages.

With wav2vec 2.0, we can also also be taught speech units which can be dilapidated across languages. We web that some units are dilapidated for fully a particular language, whereas others are dilapidated in equivalent languages and most regularly even in languages that aren’t very equivalent.

Performance on public speech benchmarks

We educated wav2vec on 960 hours of unannotated speech recordsdata from the LibriSpeech benchmark, which comprises public audiobooks. After pretraining, we pretty-tuned the model on 100 hours, 1 hour, or factual 10 minutes of annotated recordsdata from Libri-gentle to spoil speech recognition. The final consequence reveals a big enchancment over the old hiss of the artwork on 100 hours of annotated recordsdata (Noisy Pupil practicing) when wav2vec 2.0 makes exercise of the equivalent quantity of annotated recordsdata. Furthermore, it peaceable reveals enchancment over the old easiest consequence even when the exercise of 100x less annotated recordsdata, or factual one hour.

What occurs if we develop the amount of unannotated recordsdata? To respond to this demand, we educated on 53K hours of unannotated recordsdata from the LibriVox recordsdata station (a big series of public audiobooks) and pretty-tuned with fully 10 minutes of labeled recordsdata. The final consequence turned into a model that also finished a WER of 8.6 p.c. This demonstrates that wav2vec 2.0 can enable speech recognition units for settings the build there’s terribly shrimp labeled practicing recordsdata.

WER for Noisy Pupil self-practicing with 100 hours of labeled recordsdata. Wav2vec 2.0 with 100 hours, 1 hour, and fully 10 minutes of labeled recordsdata. All units exercise the the relaxation of the LibriSpeech corpus (total 960 hours) as unannotated recordsdata, with the exception of for the closing consequence, which makes exercise of 53K hours from LibriVox.

To review injurious-linguality, we educated wav2vec 2.0 on unannotated speech audio of 12 languages from the Well-liked Teach benchmark. The resulting manner, known as XLSR, reveals that injurious-lingual practicing dramatically improves performance on low-useful resource languages, in contrast with practicing fully on a single language. We also measured how frequently the learned speech units are dilapidated in every language and visualized the tip consequence in a 2D space. This illustration reveals that related languages are inclined to make exercise of equivalent units, which confirms that our model learns injurious-lingual units.

Results on the Well-liked Teach benchmark by technique of phoneme error rate (PER), evaluating practicing on every language in my opinion (XLSR-Mono) with practicing on all 10 languages concurrently (XLSR-10).

Visualization of how the learned units are dilapidated across languages. Graph reveals a 2D PCA space of how units are dilapidated in every language. Languages nearer to every moderately about a, admire English and German or Basque and Catalan, are inclined to make exercise of equivalent units.

The trend forward for wav2vec

Wav2vec 2.0 enables us to effect greater speech recognition programs for many more languages and domains with great less annotated recordsdata. We’ve launch-sourced the code and pretrained units to enable moderately about a researchers to effect exactly this. The code is segment of fairseq, Facebook AI Study’s sequence modeling toolkit, which supplies implementations for a wide selection of our analysis papers. A number of instructions enable practicing and pretty-tuning of the supplied units.

We are brooding about the capability of extremely effective speech representations for numerous functions, similar to speech translation, and units interesting moderately about a modalities, similar to imaginative and prescient. We are also adapting our wav2vec 2.0 implementation to flee on Cloud TPUs — dangle tuned for more recordsdata on that release in the spoil.

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