Instructions to use ovieyra21/tt-mbm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ovieyra21/tt-mbm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ovieyra21/tt-mbm")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("ovieyra21/tt-mbm") model = AutoModelForTextToSpectrogram.from_pretrained("ovieyra21/tt-mbm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ovieyra21/tt-mbm: direct link, hf CLI and curl.
- Browser
- Download file 433 Bytes
-
https://huggingface.co/ovieyra21/tt-mbm/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://ovieyra21/tt-mbm/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ovieyra21/tt-mbm/resolve/main/preprocessor_config.json
433 Bytes
| { | |
| "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, | |
| "processor_class": "SpeechT5Processor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "win_function": "hann_window", | |
| "win_length": 64 | |
| } | |