Automatic Speech Recognition
Transformers
Safetensors
English
asr_model
feature-extraction
asr
speech-recognition
speech-to-text
audio
speech-llm
word-timestamps
speaker-diarization
qwen
granite-speech
custom_code
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 AutoModel model = AutoModel.from_pretrained("mazesmazes/tiny-audio", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from mazesmazes/tiny-audio: direct link, hf CLI and curl.
- Browser
- Download file 576 Bytes
-
https://huggingface.co/mazesmazes/tiny-audio/resolve/main/requirements.txt
- Command line
-
hf download hf://mazesmazes/tiny-audio/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/mazesmazes/tiny-audio/resolve/main/requirements.txt
576 Bytes
| # Core dependencies for tiny-audio model inference | |
| # This file is pushed to HuggingFace for model repository | |
| # Transcription and word timestamps (Qwen3-ForcedAligner) need transformers >= 5.17. | |
| # Speaker diarization (return_speakers=True) uses Nemotron-3-Diarization, which is in | |
| # transformers main after 5.17 -- pinned to the commit the pipeline was verified on. | |
| transformers @ git+https://github.com/huggingface/transformers@91ea1192a00e0ebb684400434375fbd2345c8449 | |
| torch | |
| torchaudio | |
| peft | |
| # Nemotron's streaming feature extractor (return_speakers=True) imports it. | |
| librosa | |