Automatic Speech Recognition
Transformers
Safetensors
PyTorch
English
whisper
asr
multitask
intensity-regression
loudness
lufs
dbfs
pyloudnorm
jiwer
gradio
sagemaker
custom_code
Instructions to use Amirhossein75/speech-intensity-whisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amirhossein75/speech-intensity-whisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Amirhossein75/speech-intensity-whisper", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Amirhossein75/speech-intensity-whisper", trust_remote_code=True) model = AutoModelForCTC.from_pretrained("Amirhossein75/speech-intensity-whisper", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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### Model Description
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- **Developed by:** Amirhossein Yousefi (GitHub: @amirhossein-yousefi)
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- **Funded by :** Not disclosed
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- **Shared by :** Amirhossein Yousefi
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- **Model type:** Whisper encoder–decoder (ASR) with an additional regression head on the encoder for loudness prediction
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- **Language(s) (NLP):** English by default (LibriSpeech). Multilingual is supported if trained on Common Voice with the appropriate `--language` code.
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### Model Description
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- **Developed by:** Amirhossein Yousefi (GitHub: @amirhossein-yousefi)
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- **Shared by :** Amirhossein Yousefi
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- **Model type:** Whisper encoder–decoder (ASR) with an additional regression head on the encoder for loudness prediction
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- **Language(s) (NLP):** English by default (LibriSpeech). Multilingual is supported if trained on Common Voice with the appropriate `--language` code.
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