Automatic Speech Recognition
Transformers
Safetensors
prosody_to_text
feature-extraction
prosody
speech-recognition
custom_code
Instructions to use Daporte/prosody2text-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daporte/prosody2text-weights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Daporte/prosody2text-weights", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Daporte/prosody2text-weights", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
6 days ago
-
Reduce to the dataset-evaluation path, and stop trimming dataset clips
Two changes, one of which fixes a real measurement error. trim_to_speech ran by default on dataset audio. It exists for live microphone input, which arrives with room noise around the words; a dataset clip is already cut to the utterance, so re-cutting it on this pipeline's own intensity estimate takes speech with it -- and it is a step the published runs never performed, so the WER it produced was not comparable with theirs. Measured on four yodas-granary utterances: corpus WER 49.30% with the trim, 45.98% without, and one utterance went 84.8% -> 60.6%, recovering "confirm the status" and "approve or not" that the trim had been cutting off. It is gone from this repo entirely, along with the --trim flag and the trim= parameter on extract_features and transcribe. And the stroke code is gone: midi_to_hz, pressure_to_db, resample_stroke, smooth, features_from_stroke, pad_with_silence, concatenate and the drawable MIDI/silence constants. Resampling pen-tablet events has no place in a mod…a7f5301 -
Upload speech.py with huggingface_hub
Verified signature81cddba