Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Czech
whisper
whisper-event
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use mikr/whisper-medium-cs-cv11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikr/whisper-medium-cs-cv11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mikr/whisper-medium-cs-cv11")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mikr/whisper-medium-cs-cv11") model = AutoModelForSpeechSeq2Seq.from_pretrained("mikr/whisper-medium-cs-cv11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mikr/whisper-medium-cs-cv11: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/mikr/whisper-medium-cs-cv11/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://mikr/whisper-medium-cs-cv11@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mikr/whisper-medium-cs-cv11/resolve/refs%2Fpr%2F2/pytorch_model.bin
3.06 GB
- Xet hash:
- 46bad651bb92669d0dd740aa2fb7966a88e18065c51e0e09f3f7d2f8122cd046
- Size of remote file:
- 3.06 GB
- SHA256:
- 815ffebdb04f55265873e6ea503118008ad9bd4f7b29a84a8561ce3bc2d6e5b1
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