Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Czech
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use mikr/whisper-audio-concat-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikr/whisper-audio-concat-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mikr/whisper-audio-concat-test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mikr/whisper-audio-concat-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("mikr/whisper-audio-concat-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mikr/whisper-audio-concat-test: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/mikr/whisper-audio-concat-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mikr/whisper-audio-concat-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mikr/whisper-audio-concat-test/resolve/main/pytorch_model.bin
3.09 GB
- Xet hash:
- 3895bbd96a901433132b252e93b58bce5af404c91a1ab7f792779cec18785ccc
- Size of remote file:
- 3.09 GB
- SHA256:
- ac5e4723738d7219e9a0ab68c08216a06692ed18e6ee7794de64966bcb08ff62
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