Automatic Speech Recognition
Transformers
Safetensors
English
musci
text-generation
speech-to-text
asr
speech
english
qwen3
audio
reinforcement-learning
custom_code
Eval Results (legacy)
Eval Results
Instructions to use Musci-research/Musci-ASR-2.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Musci-research/Musci-ASR-2.4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Musci-research/Musci-ASR-2.4B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Musci-research/Musci-ASR-2.4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- qwen3
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- audio
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- reinforcement-learning
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license: apache-2.0
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---
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The model has approximately 2.4B parameters and is distributed as a single `bfloat16` safetensors shard of approximately 4.84 GB.
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## Model Details
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- **Developed by:** Musci Research
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- qwen3
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- audio
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- reinforcement-learning
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datasets:
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- openslr/librispeech_asr
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- speechcolab/gigaspeech
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- mozilla-foundation/common_voice_17_0
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- facebook/voxpopuli
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- LIUM/tedlium
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- edinburghcstr/ami
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- anton-l/earnings22
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- kensho/spgispeech
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metrics:
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- wer
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model-index:
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- name: Musci-ASR-2.4B
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results:
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- task:
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type: automatic-speech-recognition
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dataset:
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name: Open ASR Leaderboard
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type: hf-audio/esb-datasets-test-only-sorted
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metrics:
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- type: wer
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value: 5.44
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name: Average WER
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license: apache-2.0
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---
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The model has approximately 2.4B parameters and is distributed as a single `bfloat16` safetensors shard of approximately 4.84 GB.
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## Model Details
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- **Developed by:** Musci Research
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