Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Russian
gigaam-ctc
asr
gigaam
stt
ctc
audio
speech
custom_code
Instructions to use Den4ikAI/gigaam-ctc-whisperx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Den4ikAI/gigaam-ctc-whisperx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Den4ikAI/gigaam-ctc-whisperx", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Den4ikAI/gigaam-ctc-whisperx", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Den4ikAI/gigaam-ctc-whisperx: direct link, hf CLI and curl.
- Browser
- Download file 534 Bytes
-
https://huggingface.co/Den4ikAI/gigaam-ctc-whisperx/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Den4ikAI/gigaam-ctc-whisperx/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Den4ikAI/gigaam-ctc-whisperx/resolve/main/preprocessor_config.json
534 Bytes
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "GigaAMFeatureExtractor", | |
| "feature_extractor_class": "GigaAMFeatureExtractor", | |
| "feature_size": 64, | |
| "hop_length": 160, | |
| "n_samples": 480000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "auto_map": { | |
| "AutoFeatureExtractor": "gigaam_transformers.GigaAMFeatureExtractor", | |
| "AutoProcessor": "gigaam_transformers.GigaAMProcessor" | |
| }, | |
| "processor_class": "GigaAMProcessor", | |
| "model_type": "gigaam-ctc" | |
| } | |