Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
Commit ·
1c67fc5
1
Parent(s): 169d4a4
fixed the model_max_length value to 448
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -12980,7 +12980,7 @@
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| 12980 |
"clean_up_tokenization_spaces": true,
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| 12981 |
"eos_token": "<|endoftext|>",
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| 12982 |
"errors": "replace",
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| 12983 |
-
"model_max_length":
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| 12984 |
"pad_token": "<|endoftext|>",
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| 12985 |
"processor_class": "WhisperProcessor",
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| 12986 |
"return_attention_mask": false,
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| 12980 |
"clean_up_tokenization_spaces": true,
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| 12981 |
"eos_token": "<|endoftext|>",
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| 12982 |
"errors": "replace",
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| 12983 |
+
"model_max_length": 448,
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| 12984 |
"pad_token": "<|endoftext|>",
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| 12985 |
"processor_class": "WhisperProcessor",
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| 12986 |
"return_attention_mask": false,
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