Automatic Speech Recognition
Transformers
Safetensors
English
asr_model
asr
speech-recognition
speech-to-text
audio
speech-llm
word-timestamps
speaker-diarization
qwen
granite-speech
lora
custom_code
Eval Results (legacy)
Instructions to use mazesmazes/tiny-audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mazesmazes/tiny-audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mazesmazes/tiny-audio", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("mazesmazes/tiny-audio", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mazesmazes/tiny-audio: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/mazesmazes/tiny-audio/resolve/main/training_args.bin
- Command line
-
hf download hf://mazesmazes/tiny-audio/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mazesmazes/tiny-audio/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 1a95f0f5f9b7f29ddc69a604b9e139dffa518035abf289303dc165dff378db22
- Size of remote file:
- 5.27 kB
- SHA256:
- 96b511d201742d6e8622c5e93d5c85dd609a040ae7daa77c2d16b57834876ab0
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