Automatic Speech Recognition
Transformers
Safetensors
English
moss
text-generation
speech
english
qwen3
audio
reinforcement-learning
custom_code
Eval Results (legacy)
Eval Results
Instructions to use OpenMOSS-Team/MOSS-Transcribe-preview-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-Transcribe-preview-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="OpenMOSS-Team/MOSS-Transcribe-preview-2B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/MOSS-Transcribe-preview-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Replace TTS-copied README with ASR-correct version (fix wrong-project badges, update model-index dataset id, drop placeholder image)
Browse files
README.md
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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/
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metrics:
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- type: wer
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value: 4.87
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# MOSS-Transcribe-preview-2B
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MOSS-Transcribe-preview-2B is an English speech-to-text model that pairs a Qwen3-1.7B-base language-model backbone with a Qwen3-Omni-MoE audio encoder. A gated-MLP adapter projects audio features into the language-model embedding space. The model is trained on public English ASR corpora and fine-tuned with reinforcement learning on the Open ASR Leaderboard training splits.
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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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test sets. Predictions are produced with greedy decoding (`num_beams=1`,
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`max_new_tokens=512`), a single dataset-agnostic chat template, and scored with
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the leaderboard's standardized scoring (English normalizer + word-level edit
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distance with compound merging). TED-LIUM is
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and is therefore excluded.
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| Dataset | WER (%) |
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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/open-asr-leaderboard
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metrics:
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- type: wer
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value: 4.87
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# MOSS-Transcribe-preview-2B
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<div align="center">
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<a href="https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-preview-2B"><img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface"></a>
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<a href="https://huggingface.co/spaces/hf-audio/open_asr_leaderboard"><img src="https://img.shields.io/badge/Open%20ASR%20Leaderboard-Listing-blue?logo=huggingface"></a>
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<a href="https://mosi.cn/#models"><img src="https://img.shields.io/badge/Blog-View-blue?logo=internet-explorer&"></a>
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<a href="https://x.com/Open_MOSS"><img src="https://img.shields.io/badge/Twitter-Follow-black?logo=x&"></a>
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<a href="https://discord.gg/fvm5TaWjU3"><img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&"></a>
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</div>
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MOSS-Transcribe-preview-2B is an English speech-to-text model that pairs a Qwen3-1.7B-base language-model backbone with a Qwen3-Omni-MoE audio encoder. A gated-MLP adapter projects audio features into the language-model embedding space. The model is trained on public English ASR corpora and fine-tuned with reinforcement learning on the Open ASR Leaderboard training splits.
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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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test sets. Predictions are produced with greedy decoding (`num_beams=1`,
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`max_new_tokens=512`), a single dataset-agnostic chat template, and scored with
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the leaderboard's standardized scoring (English normalizer + word-level edit
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distance with compound merging). TED-LIUM is not currently part of the
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leaderboard run and is therefore excluded.
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| Dataset | WER (%) |
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