Spaces:
Sleeping
Sleeping
| title: Fillersark | |
| emoji: 📉 | |
| colorFrom: pink | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.28.0 | |
| app_file: app.py | |
| pinned: false | |
| license: other | |
| short_description: filler | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 🎙️ CrisperWhisper Speech-to-Text | |
| This Hugging Face Space provides a speech-to-text transcription service powered by the [nyrahealth/CrisperWhisper](https://huggingface.co/nyrahealth/CrisperWhisper) model. Upload audio files and get transcribed text with word-level timestamps. | |
| ## Features | |
| - Transcribe audio files to text with word-level timestamps | |
| - Support for multiple audio formats (MP3, WAV, M4A, OGG, FLAC) | |
| - Up to 30MB file size support | |
| - Simple web interface using Gradio | |
| - REST API endpoint for programmatic access | |
| ## How to Use | |
| 1. Upload an audio file using the interface | |
| 2. Click "Transcribe" | |
| 3. View both the plain text transcription and detailed JSON output with timestamps | |
| ## API Usage | |
| You can also use this Space programmatically via the REST API: | |
| ```python | |
| import requests | |
| url = "https://your-space-name.hf.space/api/predict" | |
| files = {'audio_input': open('/path/to/your-audio-file.mp3', 'rb')} | |
| response = requests.post(url, files=files) | |
| print(response.json()) | |
| ``` | |
| ## Model Details | |
| This app uses the [nyrahealth/CrisperWhisper](https://huggingface.co/nyrahealth/CrisperWhisper) model, which is optimized for high-quality speech transcription with timestamp information. | |
| ## System Requirements | |
| For optimal performance, this Space should be run with: | |
| - GPU acceleration | |
| - At least 8GB RAM | |
| --- | |
| tags: | |
| - speech-to-text | |
| - transcription | |
| - whisper | |
| - gradio | |
| - audio-processing | |