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 ·
ff14006
1
Parent(s): 6ee9f09
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart.
Maximum crossload output difference=7.688e-04; Maximum crossload hidden layer difference=2.106e-03;
Maximum conversion output difference=7.688e-04; Maximum conversion hidden layer difference=2.106e-03;
CAUTION: The maximum admissible error was manually increased to 0.009!
- tf_model.h5 +3 -0
tf_model.h5
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oid sha256:4144af98f9f31730fd565591248e046f56a6adbe1461d7306339769e09ec8ed0
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size 151253960
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