Automatic Speech Recognition
Transformers
PyTorch
Safetensors
French
wav2vec2
audio
speech
phonemize
phoneme
Eval Results (legacy)
Instructions to use Cnam-LMSSC/wav2vec2-french-phonemizer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cnam-LMSSC/wav2vec2-french-phonemizer-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cnam-LMSSC/wav2vec2-french-phonemizer-v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cnam-LMSSC/wav2vec2-french-phonemizer-v2") model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/wav2vec2-french-phonemizer-v2") - Notebooks
- Google Colab
- Kaggle
Update README.md (#4)
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## Training procedure
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The model has been finetuned on Commonvoice-v13 (FR) for 14 epochs on a 1xADA_6000 GPU at Cnam/
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- Learning rate schedule : Double Tri-state schedule
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- Warmup from 1e-5 for 7% of total updates
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## Training procedure
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The model has been finetuned on Commonvoice-v13 (FR) for 14 epochs on a 1xADA_6000 GPU at Cnam/LMSSC using a ddp strategy and gradient-accumulation procedure (256 audios per update, corresponding roughly to 25 minutes of speech per update -> 2k updates per epoch)
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- Learning rate schedule : Double Tri-state schedule
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- Warmup from 1e-5 for 7% of total updates
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