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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.
The model has approximately 2.4B parameters and is distributed as a single bfloat16 safetensors shard of approximately 4.84 GB.
bfloat16 safetensorsThis model is intended for English automatic speech recognition, including transcription of English speech audio for research and evaluation purposes.
Evaluated on the Open ASR Leaderboard
test sets. Predictions are produced with greedy decoding (num_beams=1,
max_new_tokens=512), a single dataset-agnostic chat template, and scored with
the leaderboard's standardized scoring (English normalizer + word-level edit
distance with compound merging). TED-LIUM is not currently part of the
leaderboard run and is therefore excluded.
The model was trained on public English ASR corpora and fine-tuned with reinforcement learning on the Open ASR Leaderboard training splits.
The model is designed for English ASR. It may perform worse on non-English speech, heavy accents, noisy recordings, overlapping speakers, far-field audio, domain-specific terminology, or audio conditions that differ significantly from the training and evaluation data. The output should be manually reviewed before use in high-stakes settings.
This model is released under the Apache-2.0 license.
import librosaimport torchfrom huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom transformers.dynamic_module_utils import get_class_from_dynamic_module
REPO = "OpenMOSS-Team/MOSS-Transcribe-preview-2B"DEVICE = "cuda:0"
model = AutoModelForCausalLM.from_pretrained( REPO, dtype=torch.bfloat16, trust_remote_code=True).to(DEVICE).eval()tokenizer = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
MossProcessor = get_class_from_dynamic_module("processing_Moss.MossProcessor", REPO)MelConfig = get_class_from_dynamic_module("processing_Moss.MelConfig", REPO)
mel_cfg = MelConfig( mel_sr=16000, mel_dim=128, mel_n_fft=400, mel_hop_length=160,)processor = MossProcessor(tokenizer, config=mel_cfg, enable_time_marker=False)processor.load_template(hf_hub_download(REPO, "chat_template_default.py"))
waveform, _ = librosa.load("your_audio.wav", sr=16000)inputs = processor(audio=waveform, return_tensors="pt").to(DEVICE)inputs["audio_data"] = inputs["audio_data"].to(model.dtype)
with torch.no_grad(): out_ids = model.generate( **inputs, max_new_tokens=512, do_sample=False, num_beams=1, use_cache=True, eos_token_id=[processor.end_token_id], )
new_ids = out_ids[:, inputs["input_ids"].shape[1]:]transcript = processor.batch_decode(new_ids, skip_special_tokens=True)[0].strip()print(transcript)@misc{moss_transcribe_2025, title = {{MOSS-Transcribe-preview-2B}}, author = {{OpenMOSS Team}}, year = {2025}, howpublished = {\url{https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-preview-2B}}}