Instructions to use AndreaNotes1/xqieech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreaNotes1/xqieech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="AndreaNotes1/xqieech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("AndreaNotes1/xqieech") model = AutoModelForTextToSpectrogram.from_pretrained("AndreaNotes1/xqieech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from AndreaNotes1/xqieech: direct link, hf CLI and curl.
- Browser
- Download file 492 Bytes
-
https://huggingface.co/AndreaNotes1/xqieech/resolve/main/processor_config.json
- Command line
-
hf download hf://AndreaNotes1/xqieech/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/AndreaNotes1/xqieech/resolve/main/processor_config.json
492 Bytes
| { | |
| "feature_extractor": { | |
| "do_normalize": false, | |
| "feature_extractor_type": "SpeechT5FeatureExtractor", | |
| "feature_size": 1, | |
| "fmax": 7600, | |
| "fmin": 80, | |
| "frame_signal_scale": 1.0, | |
| "hop_length": 16, | |
| "mel_floor": 1e-10, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "win_function": "hann_window", | |
| "win_length": 64 | |
| }, | |
| "processor_class": "SpeechT5Processor" | |
| } | |