Automatic Speech Recognition
Transformers
PyTorch
whisper
audio
speech
wav2vec2
Eval Results (legacy)
Instructions to use devasheeshG/whisper_large_v2_fp16_transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devasheeshG/whisper_large_v2_fp16_transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devasheeshG/whisper_large_v2_fp16_transformers")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devasheeshG/whisper_large_v2_fp16_transformers") model = AutoModelForSpeechSeq2Seq.from_pretrained("devasheeshG/whisper_large_v2_fp16_transformers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 251e918
Update README.md
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README.md
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description: Character Error Rate
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widget:
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- example_title: Librispeech sample 1
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
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- example_title: Librispeech sample 2
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description: Character Error Rate
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widget:
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- example_title: Hinglish Sample
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src: https://huggingface.co/devasheeshG/whisper_medium_fp16_transformers/resolve/main/test.wav
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- example_title: Librispeech sample 1
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
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- example_title: Librispeech sample 2
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