Instructions to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration: direct link, hf CLI and curl.
- Browser
- Download file 413 Bytes
-
https://huggingface.co/onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration/resolve/main/processor_config.json
- Command line
-
hf download hf://onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration/resolve/main/processor_config.json
413 Bytes
| { | |
| "audio_processor": { | |
| "feature_extractor_type": "GraniteSpeechFeatureExtractor", | |
| "melspec_kwargs": { | |
| "hop_length": 160, | |
| "n_fft": 512, | |
| "n_mels": 8, | |
| "sample_rate": 16000, | |
| "win_length": 400 | |
| }, | |
| "projector_downsample_rate": 1, | |
| "projector_window_size": 3, | |
| "sampling_rate": 16000 | |
| }, | |
| "audio_token": "<|audio|>", | |
| "processor_class": "GraniteSpeechProcessor" | |
| } | |