Instructions to use harpertoken/talk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harpertoken/talk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="harpertoken/talk")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("harpertoken/talk") model = AutoModelForSpeechSeq2Seq.from_pretrained("harpertoken/talk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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> Previously published as `harpertoken/harpertokenASR`. The old identifier still redirects here.
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# talk
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A `whisper-small` encoder-decoder, fine-tuned on short passages of live microphone audio, for English speech recognition at 16 kHz mono. The weights are float32 and occupy roughly 967 MB.
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- audio
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# talk
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A `whisper-small` encoder-decoder, fine-tuned on short passages of live microphone audio, for English speech recognition at 16 kHz mono. The weights are float32 and occupy roughly 967 MB.
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