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[MMS] Create Colab notebook for LID inference #5157

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May 25, 2023
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[MMS] Create Colab notebook for LID inference #5157

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[MMS] Create Colab Notebook for LID task
vineelpratap May 25, 2023
be4d2b0
Update README.md
vineelpratap May 25, 2023
33e9284
Update README.md
vineelpratap May 25, 2023
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23 changes: 12 additions & 11 deletions examples/mms/README.md
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Expand Up @@ -6,16 +6,6 @@ You can find details in the paper [Scaling Speech Technology to 1000+ languages]

An overview of the languages covered by MMS can be found [here](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html).


## Pretrained models

| Model | Link
|---|---
MMS-300M | [download](https://dl.fbaipublicfiles.com/mms/pretraining/base_300m.pt)
MMS-1B | [download](https://dl.fbaipublicfiles.com/mms/pretraining/base_1b.pt)

Example commands to finetune the pretrained models can be found [here](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec#fine-tune-a-pre-trained-model-with-ctc).

## Finetuned models
### ASR

Expand Down Expand Up @@ -56,7 +46,7 @@ cd /path/to/fairseq-py/
python examples/mms/asr/infer/mms_infer.py --model "/path/to/asr/model" --lang lang_code \
--audio "/path/to/audio_1.wav" "/path/to/audio_2.wav" "/path/to/audio_3.wav"
```
We also provide an Ipython notebook example in inside `asr/tutorial` folder [ipynb](https://github.com/facebookresearch/fairseq/blob/androstj-patch-1/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb) or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/fairseq/blob/main/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb)
We also provide an Ipython notebook example inside `asr/tutorial` folder [ipynb](https://github.com/facebookresearch/fairseq/blob/main/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb) or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/fairseq/blob/main/examples/mms/asr/tutorial/MMS_ASR_Inference_Colab.ipynb)


For more advance configuration and calculate CER/WER, you could prepare manifest folder by creating a folder with this format:
Expand Down Expand Up @@ -160,6 +150,17 @@ $ PYTHONPATH='.' python3 examples/mms/lid/infer.py /path/to/dict/l126/ --path
```
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`.

We also provide an Ipython notebook example inside `lid/tutorial` folder [ipynb](https://github.com/facebookresearch/fairseq/blob/main/examples/mms/lid/tutorial/MMS_LID_Inference_Colab.ipynb) or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/facebookresearch/fairseq/blob/main/examples/mms/lid/tutorial/MMS_LID_Inference_Colab.ipynb)


## Pretrained models

| Model | Link
|---|---
MMS-300M | [download](https://dl.fbaipublicfiles.com/mms/pretraining/base_300m.pt)
MMS-1B | [download](https://dl.fbaipublicfiles.com/mms/pretraining/base_1b.pt)

Example commands to finetune the pretrained models can be found [here](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec#fine-tune-a-pre-trained-model-with-ctc).

## Forced Alignment Tooling

Expand Down
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