Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
English
speech_to_text
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Classroom-workshop/assignment1-jane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classroom-workshop/assignment1-jane with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Classroom-workshop/assignment1-jane")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Classroom-workshop/assignment1-jane") model = AutoModelForSpeechSeq2Seq.from_pretrained("Classroom-workshop/assignment1-jane", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Classroom-workshop/assignment1-jane: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/Classroom-workshop/assignment1-jane/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Classroom-workshop/assignment1-jane/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Classroom-workshop/assignment1-jane/resolve/main/preprocessor_config.json
242 Bytes
| { | |
| "do_ceptral_normalize": true, | |
| "feature_size": 80, | |
| "normalize_means": true, | |
| "normalize_vars": true, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |