Automatic Speech Recognition
Transformers
ONNX
PEFT
whisper
polywhisper
indic-asr
hindi-asr
tamil-speech-recognition
telugu-stt
bengali-asr
marathi-speech-to-text
speech-recognition
multilingual
lora
hindi
tamil
telugu
bengali
marathi
indic-languages
indian-languages
speech-to-text
low-resource-asr
fleurs
indicvoices
quantized
efficient-asr
edge-asr
Eval Results (legacy)
Instructions to use eulogik/polywhisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eulogik/polywhisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eulogik/polywhisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("eulogik/polywhisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("eulogik/polywhisper", device_map="auto") - PEFT
How to use eulogik/polywhisper with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Ctrl+K
- export
- gradio_space
- paper
- polywhisper
- polywhisper_output_gpu0
- polywhisper_output_gpu0_clean
- polywhisper_output_gpu1
- polywhisper_output_gpu1_clean
- polywhisper_output_hi
- polywhisper_output_ta
- 2.03 kB
- 13.6 kB
- 9.98 kB
- 631 kB
- 296 kB
- 748 kB
- 530 kB
- 356 kB
- 10.8 kB
- 630 kB
- 302 kB
- 795 kB
- 543 kB
- 364 kB
- 1.1 kB
- 67 Bytes
- 326 Bytes
- 3.18 MB xet
- 4.18 kB
- 543 Bytes
- 3.18 MB xet
- 647 kB
- 667 kB
- 168 Bytes
- 295 kB
- 265 kB
- 292 kB
- 293 kB
- 167 Bytes
- 4.5 kB
- 819 kB
- 770 kB
- 168 Bytes
- 538 kB
- 524 kB
- 525 kB
- 167 Bytes
- 351 kB
- 342 kB
- 168 Bytes