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
Commit History
10 days ago
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Model card: current WER table, eval results (model-index), tags; list AutoModelForSpeechSeq2Seq first in auto_map
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11 days ago
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Update custom model files, README, and requirements
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Upload README.md with huggingface_hub
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Update custom model files, README, and requirements
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12 days ago
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Model card: long-audio and diarization limits, credit Nemotron and Qwen aligner
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Update custom model files, README, and requirements
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Model card: document speaker diarization
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Update model card for granite-qwen-frozen-4
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Update README from MODEL_CARD.md
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Model save
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Model save
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Model save
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Training in progress - step 1000
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