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Update blog post link for MMS (#5114)

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* Update blog post link for MMS

* Update blog post link for MMS
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Vineel Pratap 2023-05-22 12:29:19 -07:00 committed by GitHub
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README.md
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@ -70,6 +70,7 @@ We provide reference implementations of various sequence modeling papers:
</p></details>
### What's New:
* May 2023 [Released models for Scaling Speech Technology to 1,000+ Languages (Pratap, et al., 2023)](examples/mms/README.md)
* June 2022 [Released code for wav2vec-U 2.0 from Towards End-to-end Unsupervised Speech Recognition (Liu, et al., 2022)](examples/wav2vec/unsupervised/README.md)
* May 2022 [Integration with xFormers](https://github.com/facebookresearch/xformers)
* December 2021 [Released Direct speech-to-speech translation code](examples/speech_to_speech/README.md)

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examples/mms/README.md
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@ -2,7 +2,7 @@
The Massively Multilingual Speech (MMS) project expands speech technology from about 100 languages to over 1,000 by building a single multilingual speech recognition model supporting over 1,100 languages (more than 10 times as many as before), language identification models able to identify over [4,000 languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html) (40 times more than before), pretrained models supporting over 1,400 languages, and text-to-speech models for over 1,100 languages. Our goal is to make it easier for people to access information and to use devices in their preferred language.
You can find details in the paper [Scaling Speech Technology to 1000+ languages](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/) and the [blog post](https://ai.facebook.com/blog/multilingual-speech-recognition-model/).
You can find details in the paper [Scaling Speech Technology to 1000+ languages](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/) and the [blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/).
An overview of the languages covered by MMS can be found [here](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html).
@ -147,13 +147,13 @@ eng 1
eng 1
```
Download model and the corresponding dictionary file for the LID model. The following command assuming there is a file named `dict.lang.txt` in `/path/to/dict/l126/`.
Download model and the corresponding dictionary file for the LID model.
Use the following command to run inference -
```shell script
$ PYTHONPATH='.' python3 examples/mms/lid/infer.py /path/to/dict/l126/ --path /path/to/models/mms1b_l126.pt \
--task audio_classification --infer-manifest /path/to/manifest.tsv --output-path <OUTDIR>
```
`<OUTDIR>/predictions.txt` will contain the predictions from the model for the audio files in `manifest.tsv`.
The above command assumes there is a file named `dict.lang.txt` in `/path/to/dict/l126/`. `<OUTDIR>/predictions.txt` will contain the predictions from the model for the audio files in `manifest.tsv`.
# License