Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Danish
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use JulieHinge/whisper-medium-ftspeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulieHinge/whisper-medium-ftspeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JulieHinge/whisper-medium-ftspeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("JulieHinge/whisper-medium-ftspeech") model = AutoModelForSpeechSeq2Seq.from_pretrained("JulieHinge/whisper-medium-ftspeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from JulieHinge/whisper-medium-ftspeech: direct link, hf CLI and curl.
- Browser
- Download file 339 Bytes
-
https://huggingface.co/JulieHinge/whisper-medium-ftspeech/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://JulieHinge/whisper-medium-ftspeech/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/JulieHinge/whisper-medium-ftspeech/resolve/main/preprocessor_config.json
339 Bytes
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |