Qwen3-ASR-1.7B-th-fleurs

Qwen3-ASR-1.7B-th-fleurs

Thai automatic speech recognition model, full fine-tune of Qwen/Qwen3-ASR-1.7B on the Thai split of google/fleurs.

Evaluation

FLEURS Thai test split (1,021 utterances). Reported via the evaluate library — raw model output vs reference, no text normalisation.

Lower is better.

Usage

import torch
from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained(
    "PogusTheWhisper/Qwen3-ASR-1.7B-th-fleurs",
    dtype=torch.bfloat16,
    device_map="cuda:0",
    max_inference_batch_size=16,
    max_new_tokens=256,
)
results = model.transcribe(audio="path/to/audio.wav", language="Thai")
print(results[0].text)

For maximum throughput, use the vLLM backend:

model = Qwen3ASRModel.LLM(
    model="PogusTheWhisper/Qwen3-ASR-1.7B-th-fleurs",
    gpu_memory_utilization=0.7,
    max_inference_batch_size=128,
    max_new_tokens=4096,
)
results = model.transcribe(audio="path/to/audio.wav", language="Thai")
print(results[0].text)

Training

Full fine-tune (FFT) of the entire 1.7B-parameter base model using the official QwenLM/Qwen3-ASR qwen3_asr_sft.py script.

Limitations

• Trained on read-speech only — FLEURS is broadcast / audiobook style. Conversational, noisy, and telephony-audio performance is not measured.
• 16 kHz mono audio required (matches the base model).
• Code-switched audio (Thai + English) inherits base-model behaviour; not specifically tuned.
• Small training set (~7 hours). For production use, consider mixing with Common Voice Thai or your own labelled data.

Citation

@misc{qwen3asr,
  title  = {Qwen3-ASR},
  author = {Qwen Team},
  year   = {2025},
  url    = {https://huggingface.co/Qwen/Qwen3-ASR-1.7B}
}

@inproceedings{conneau2023fleurs,
  title     = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
  author    = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
  booktitle = {SLT},
  year      = {2023}
}