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examples/example_qwen3_asr_transformers.py

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+# coding=utf-8
+# Copyright 2026 The Alibaba Qwen team.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Examples for Qwen3ASRModel (Transformers backend).
+
+Covers:
+  - single-sample inference (URL audio)
+  - batch inference (mixed URL / base64 / (np.ndarray, sr))
+  - forcing language (text-only output)
+  - returning time_stamps (single + batch) via Qwen3ForcedAligner
+"""
+
+import base64
+import io
+import urllib.request
+from typing import Tuple
+
+import numpy as np
+import soundfile as sf
+import torch
+
+from qwen_asr import Qwen3ASRModel
+
+
+ASR_MODEL_PATH = "Qwen/Qwen3-ASR-1.7B"
+FORCED_ALIGNER_PATH = "Qwen/Qwen3-ForcedAligner-0.6B"
+
+URL_ZH = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav"
+URL_EN = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
+
+
+def _download_audio_bytes(url: str, timeout: int = 30) -> bytes:
+    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        return resp.read()
+
+
+def _read_wav_from_bytes(audio_bytes: bytes) -> Tuple[np.ndarray, int]:
+    with io.BytesIO(audio_bytes) as f:
+        wav, sr = sf.read(f, dtype="float32", always_2d=False)
+    return np.asarray(wav, dtype=np.float32), int(sr)
+
+
+def _to_data_url_base64(audio_bytes: bytes, mime: str = "audio/wav") -> str:
+    b64 = base64.b64encode(audio_bytes).decode("utf-8")
+    return f"data:{mime};base64,{b64}"
+
+
+def _print_result(title: str, results) -> None:
+    print(f"\n===== {title} =====")
+    for i, r in enumerate(results):
+        print(f"[sample {i}] language={r.language!r}")
+        print(f"[sample {i}] text={r.text!r}")
+        if r.time_stamps is not None and len(r.time_stamps) > 0:
+            head = r.time_stamps[0]
+            tail = r.time_stamps[-1]
+            print(f"[sample {i}] ts_first: {head.text!r} {head.start_time}->{head.end_time} s")
+            print(f"[sample {i}] ts_last : {tail.text!r} {tail.start_time}->{tail.end_time} s")
+
+
+def test_single_url(asr: Qwen3ASRModel) -> None:
+    results = asr.transcribe(
+        audio=URL_ZH,
+        language=None,
+        return_time_stamps=False,
+    )
+    assert isinstance(results, list) and len(results) == 1
+    _print_result("single-url (no forced language, no timestamps)", results)
+
+
+def test_batch_mixed(asr: Qwen3ASRModel) -> None:
+    zh_bytes = _download_audio_bytes(URL_ZH)
+    en_bytes = _download_audio_bytes(URL_EN)
+
+    zh_b64 = _to_data_url_base64(zh_bytes, mime="audio/wav")
+    en_wav, en_sr = _read_wav_from_bytes(en_bytes)
+
+    results = asr.transcribe(
+        audio=[URL_ZH, zh_b64, (en_wav, en_sr)],
+        context=["", "交易 停滞", ""],
+        language=[None, "Chinese", "English"],
+        return_time_stamps=False,
+    )
+    assert len(results) == 3
+    _print_result("batch-mixed (forced language for some)", results)
+
+
+def test_single_with_timestamps(asr: Qwen3ASRModel) -> None:
+    results = asr.transcribe(
+        audio=URL_EN,
+        language="English",
+        return_time_stamps=True,
+    )
+    assert len(results) == 1
+    assert results[0].time_stamps is not None
+    _print_result("single-url (forced language + timestamps)", results)
+
+
+def test_batch_with_timestamps(asr: Qwen3ASRModel) -> None:
+    zh_bytes = _download_audio_bytes(URL_ZH)
+    zh_b64 = _to_data_url_base64(zh_bytes, mime="audio/wav")
+
+    results = asr.transcribe(
+        audio=[URL_ZH, zh_b64, URL_EN],
+        context=["", "交易 停滞", ""],
+        language=["Chinese", "Chinese", "English"],
+        return_time_stamps=True,
+    )
+    assert len(results) == 3
+    assert all(r.time_stamps is not None for r in results)
+    _print_result("batch (forced language + timestamps)", results)
+
+
+def main() -> None:
+    asr = Qwen3ASRModel.from_pretrained(
+        ASR_MODEL_PATH,
+        dtype=torch.bfloat16,
+        device_map="cuda:0",
+        # attn_implementation="flash_attention_2",
+        forced_aligner=FORCED_ALIGNER_PATH,
+        forced_aligner_kwargs=dict(
+            dtype=torch.bfloat16,
+            device_map="cuda:0",
+            # attn_implementation="flash_attention_2",
+        ),
+        max_inference_batch_size=32,
+        max_new_tokens=256,
+    )
+
+    test_single_url(asr)
+    test_batch_mixed(asr)
+    test_single_with_timestamps(asr)
+    test_batch_with_timestamps(asr)
+
+
+if __name__ == "__main__":
+    main()