Instructions to use ThomasFG/67.5-67.5-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/67.5-67.5-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/67.5-67.5-medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/67.5-67.5-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/67.5-67.5-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download args.json from ThomasFG/67.5-67.5-medium: direct link, hf CLI and curl.
- Browser
- Download file 478 Bytes
-
https://huggingface.co/ThomasFG/67.5-67.5-medium/resolve/main/args.json
- Command line
-
hf download hf://ThomasFG/67.5-67.5-medium/args.json
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curl -L -o args.json https://huggingface.co/ThomasFG/67.5-67.5-medium/resolve/main/args.json
478 Bytes
| {"pretrained_model": "openai/whisper-medium", "dataset_path": "./data", "output_dir": "./fine-tuned-whisper", "num_train_epochs": 1, "train_batch_size": 64, "gradient_accumulation_steps": 1, "eval_batch_size": 8, "max_learning_rate": 1e-05, "warmup_steps": 0, "max_steps": 0, "eval_steps": null, "gradient_checkpointing": true, "fp16": true, "evaluation_strategy": "epoch", "logging_steps": 500, "save_steps": 1000, "dataset": "mixed", "real": 67.5, "fake": 67.5, "patience": 5} |