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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: utter-project/mHuBERT-147 |
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datasets: |
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- FBK-MT/Speech-MASSIVE |
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- FBK-MT/Speech-MASSIVE-test |
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- mozilla-foundation/common_voice_17_0 |
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- google/fleurs |
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language: |
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- fr |
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metrics: |
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- wer |
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- cer |
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pipeline_tag: automatic-speech-recognition |
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--- |
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**This is a CTC-based Automatic Speech Recognition system for French.** |
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This model is part of the SLU demo available here: [LINK TO THE DEMO GOES HERE] |
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It is based on the [mHuBERT-147](https://huggingface.co/utter-project/mHuBERT-147) speech foundation model. |
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* Training data: XX hours |
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* Normalization: Whisper normalization |
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* Performance: |
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# Table of Contents: |
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1. Training Parameters |
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2. [ASR Model class](https://huggingface.co/naver/mHuBERT-147-ASR-fr#ASR-Model-class) |
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3. Running inference |
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## Training Parameters |
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The training parameters are available in config.yaml. |
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We downsample the commonvoice dataset to 70,000 utterances. |
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## ASR Model class |
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We use the mHubertForCTC class for our model, which is nearly identical to the existing HubertForCTC class. |
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The key difference is that we've added a few additional hidden layers at the end of the Transformer stack, just before the lm_head. |
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The code is available in [CTC_model.py](https://huggingface.co/naver/mHuBERT-147-ASR-fr/blob/main/CTC_model.py). |
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## Running inference |
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The run_asr.py file illustrates how to load the model for inference (**load_asr_model**), and how to produce transcription for a file (**run_asr_inference**). |
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Please follow the requirements.txt to avoid incorrect model loading. |
