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
File size: 576 Bytes
cf1aff8 37f59d3 cf1aff8 26ce2ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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 |