whisper_large_v3_turbo

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Run Whisper with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/whisper_large_v3_turbo

Paper: Robust Speech Recognition via Large-Scale Weak Supervision (arXiv:2212.04356) · HF Papers

Whisper is a multilingual encoder-decoder ASR model trained on large-scale weak supervision. Use task="transcribe" to keep the source language or task="translate" to render English. Pass language=None to let the model detect the spoken language. Output is cased and punctuated.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of openai/whisper-large-v3-turbo for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an ASR checkpoint (WhisperConditionalGenerate, 4 decoder layers).

✨ Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

import soundfile as sf
from zeromodels.models.whisper import (
    WhisperProcessor,
    WhisperConditionalGenerate,
)

model = WhisperConditionalGenerate.from_weights("zeromodels/whisper_large_v3_turbo")
processor = WhisperProcessor.from_weights("zeromodels/whisper_large_v3_turbo")

audio, sr = sf.read("your_audio.wav", dtype="float32")  # 16 kHz mono
# task="transcribe" keeps the source language; "translate" -> English.
text = model.generate(audio, processor, language="en", task="transcribe")
print(repr(text[0]))

Load any Whisper variant the same way with from_weights("zeromodels/<variant>"):

Tips

• Set KERAS_BACKEND before importing Keras / zeromodels.
• Prefer WhisperProcessor.from_weights(...) so mel bins match the variant (v3 uses 128).
• Clips are padded to a 30 s window; chunk longer audio yourself.
• See Whisper docs and Loading Weights.
• Community / upstream safetensors still work via the hf: prefix, e.g. WhisperConditionalGenerate.from_weights("hf:openai/whisper-large-v3-turbo").

Special Thanks

A huge thank you to the OpenAI Whisper authors for creating and releasing these models.

License: Apache 2.0.