Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-tiny.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny.en") - Notebooks
- Google Colab
- Kaggle
Correct long-form generation config parameters 'max_initial_timestamp_index' and 'prev_sot_token_id'.
#24
by patrickvonplaten - opened
- generation_config.json +2 -1
generation_config.json
CHANGED
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@@ -47,10 +47,11 @@
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]
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],
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"is_multilingual": false,
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-
"max_initial_timestamp_index":
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"max_length": 448,
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"no_timestamps_token_id": 50362,
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"pad_token_id": 50256,
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"return_timestamps": false,
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"suppress_tokens": [
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1,
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]
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],
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"is_multilingual": false,
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+
"max_initial_timestamp_index": 50,
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"max_length": 448,
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"no_timestamps_token_id": 50362,
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"pad_token_id": 50256,
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+
"prev_sot_token_id": 50360,
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"return_timestamps": false,
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"suppress_tokens": [
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1,
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