Instructions to use slplab/whisper-large_v2_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slplab/whisper-large_v2_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="slplab/whisper-large_v2_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("slplab/whisper-large_v2_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("slplab/whisper-large_v2_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update handler.py
Browse files- handler.py +1 -1
handler.py
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@@ -16,7 +16,7 @@ class EndpointHandler:
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#processor = AutoProcessor.from_pretrained(path)
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#self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
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#feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
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self.pipe = pipeline(task='automatic-speech-recognition', model=path
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#processor = AutoProcessor.from_pretrained(path)
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#self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
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#feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
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self.pipe = pipeline(task='automatic-speech-recognition', model=path)
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