Automatic Speech Recognition
Transformers
Safetensors
Twi
vits
text-to-audio
audio
speech
african-languages
multilingual
simba
low-resource
speech-recognition
asr
Instructions to use UBC-NLP/Simba-TTS-twi-asanti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/Simba-TTS-twi-asanti with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UBC-NLP/Simba-TTS-twi-asanti")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/Simba-TTS-twi-asanti") model = AutoModelForTextToWaveform.from_pretrained("UBC-NLP/Simba-TTS-twi-asanti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from UBC-NLP/Simba-TTS-twi-asanti: direct link, hf CLI and curl.
- Browser
- Download file 254 Bytes
-
https://huggingface.co/UBC-NLP/Simba-TTS-twi-asanti/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://UBC-NLP/Simba-TTS-twi-asanti/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/UBC-NLP/Simba-TTS-twi-asanti/resolve/main/preprocessor_config.json
254 Bytes
| { | |
| "feature_extractor_type": "VitsFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 256, | |
| "max_wav_value": 32768.0, | |
| "n_fft": 1024, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |