Instructions to use futo-org/acft-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use futo-org/acft-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="futo-org/acft-whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("futo-org/acft-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("futo-org/acft-whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| # Model Card | |
| ## Model Description | |
| This is in a fine-tuned series of [OpenAI's Whisper models](https://github.com/openai/whisper). | |
| The models have been finetuned for dynamic audio context robustness, allowing shorter audio contexts for better performance with short audio inputs. The method is detailed [in our GitHub repo](https://github.com/futo-org/whisper-acft). | |
| - **Developed by:** FUTO | |
| - **License:** Apache-2.0 | |
| - **Finetuned from model:** OpenAI Whisper | |
| ## Uses | |
| These models are not useful by themselves under default Whisper runtime configurations. | |
| The easiest way to test differing audio context is to use whisper.cpp with the `--audio-context` parameter. We provide converted whisper.cpp models in our [GitHub README](https://github.com/futo-org/whisper-acft?tab=readme-ov-file#finetuning-whisper-for-dynamic-audio-context-robustness). | |
| ## Other Information | |
| More information can be found in our [GitHub README](https://github.com/futo-org/whisper-acft?tab=readme-ov-file#finetuning-whisper-for-dynamic-audio-context-robustness). | |