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
`forced_decoder_ids` cannot be specified using transformers master branch
#43
by serena-ruan - opened
In latest transformers master branch: https://github.com/huggingface/transformers/blob/6a2627918d84f25422b931507a8fb9146106ca20/src/transformers/generation/utils.py#L1083 triggers errorValueError: You have explicitly specified `forced_decoder_ids`. Please remove the `forced_decoder_ids` argument in favour of `input_ids` or `decoder_input_ids` respectively. Could you update the model accordingly?
Please see https://github.com/huggingface/transformers/issues/37172
Still a path to a solution is not very clear.