Automatic Speech Recognition
Transformers
Safetensors
French
wav2vec2
audio
speech
phonemize
phoneme
Eval Results (legacy)
Instructions to use Cnam-LMSSC/phonemizer_headset_microphone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cnam-LMSSC/phonemizer_headset_microphone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cnam-LMSSC/phonemizer_headset_microphone")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cnam-LMSSC/phonemizer_headset_microphone") model = AutoModelForCTC.from_pretrained("Cnam-LMSSC/phonemizer_headset_microphone") - Notebooks
- Google Colab
- Kaggle
add vibravox paper on arXiV
Browse files
README.md
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- **Language:** French
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- **License:** MIT
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- **Finetuned from model:** [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2)
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- **Finetuned dataset:** `headset_microphone` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox)
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- **Samplerate for usage:** 16kHz
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## Output
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- **Language:** French
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- **License:** MIT
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- **Finetuned from model:** [facebook/wav2vec2-base-fr-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fr-voxpopuli-v2)
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- **Finetuned dataset:** `headset_microphone` audio of the `speech_clean` subset of [Cnam-LMSSC/vibravox](https://huggingface.co/datasets/Cnam-LMSSC/vibravox) (see [VibraVox paper on arXiV](https://arxiv.org/abs/2407.11828))
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- **Samplerate for usage:** 16kHz
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## Output
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