214 lines
6.2 KiB
Python
214 lines
6.2 KiB
Python
# 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 Qwen3ForcedAligner.
|
|
|
|
Covers:
|
|
- single-sample inference (URL audio)
|
|
- batch inference (URL audio)
|
|
- base64 audio input (data:audio/wav;base64,...)
|
|
- numpy waveform input as (np.ndarray, sr) using urllib request
|
|
"""
|
|
|
|
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 Qwen3ForcedAligner
|
|
|
|
|
|
MODEL_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"
|
|
|
|
TEXT_ZH = "甚至出现交易几乎停滞的情况。"
|
|
TEXT_EN = (
|
|
"Mm. Oh, yeah, yeah. He wasn't even that big when I started listening to him, "
|
|
"but and his solo music didn't do overly well, but he did very well when he "
|
|
"started writing for other people."
|
|
)
|
|
|
|
|
|
def _download_audio_bytes(url: str, timeout: int = 30) -> bytes:
|
|
"""
|
|
Download audio bytes from a URL.
|
|
|
|
Args:
|
|
url (str): Audio URL.
|
|
timeout (int): Request timeout in seconds.
|
|
|
|
Returns:
|
|
bytes: Raw response 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]:
|
|
"""
|
|
Decode audio bytes into waveform and sampling rate.
|
|
|
|
Args:
|
|
audio_bytes (bytes): Encoded audio bytes (wav/flac/ogg supported by libsndfile).
|
|
|
|
Returns:
|
|
Tuple[np.ndarray, int]: (waveform, sr). Waveform may be mono or multi-channel.
|
|
"""
|
|
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:
|
|
"""
|
|
Convert audio bytes into a base64 data URL string.
|
|
|
|
Args:
|
|
audio_bytes (bytes): Encoded audio bytes.
|
|
mime (str): MIME type.
|
|
|
|
Returns:
|
|
str: data:{mime};base64,... string.
|
|
"""
|
|
b64 = base64.b64encode(audio_bytes).decode("utf-8")
|
|
return f"data:{mime};base64,{b64}"
|
|
|
|
|
|
def _print_result(title: str, results) -> None:
|
|
"""
|
|
Print a compact summary for debugging.
|
|
|
|
Args:
|
|
title (str): Case name.
|
|
results (List[ForcedAlignResult]): Outputs from aligner.align(...).
|
|
"""
|
|
print(f"\n===== {title} =====")
|
|
for i, r in enumerate(results):
|
|
n = len(r)
|
|
head = r[0] if n > 0 else None
|
|
tail = r[-1] if n > 0 else None
|
|
print(f"[sample {i}] item={n}")
|
|
if head is not None:
|
|
print(f" first: {head.text!r} {head.start_time}->{head.end_time} s")
|
|
print(f" last : {tail.text!r} {tail.start_time}->{tail.end_time} s")
|
|
|
|
|
|
def test_single_url(aligner: Qwen3ForcedAligner) -> None:
|
|
"""
|
|
Single-sample alignment using HTTPS URL audio input.
|
|
"""
|
|
results = aligner.align(
|
|
audio=URL_ZH,
|
|
text=TEXT_ZH,
|
|
language="Chinese",
|
|
)
|
|
assert isinstance(results, list) and len(results) == 1
|
|
assert len(results[0]) > 0
|
|
_print_result("single-url", results)
|
|
|
|
|
|
def test_batch_url(aligner: Qwen3ForcedAligner) -> None:
|
|
"""
|
|
Batch alignment using HTTPS URL audio input.
|
|
"""
|
|
results = aligner.align(
|
|
audio=[URL_ZH, URL_EN],
|
|
text=[TEXT_ZH, TEXT_EN],
|
|
language=["Chinese", "English"],
|
|
)
|
|
assert len(results) == 2
|
|
assert len(results[0]) > 0 and len(results[1]) > 0
|
|
_print_result("batch-url", results)
|
|
|
|
|
|
def test_base64_data_url(aligner: Qwen3ForcedAligner) -> None:
|
|
"""
|
|
Single-sample alignment using base64 data URL audio input.
|
|
"""
|
|
audio_bytes = _download_audio_bytes(URL_ZH)
|
|
b64 = _to_data_url_base64(audio_bytes, mime="audio/wav")
|
|
|
|
results = aligner.align(
|
|
audio=b64,
|
|
text=TEXT_ZH,
|
|
language="Chinese",
|
|
)
|
|
assert len(results) == 1
|
|
assert len(results[0]) > 0
|
|
_print_result("single-base64-data-url", results)
|
|
|
|
|
|
def test_numpy_tuple_from_request(aligner: Qwen3ForcedAligner) -> None:
|
|
"""
|
|
Single-sample alignment using (np.ndarray, sr) input where waveform is obtained by HTTP request.
|
|
"""
|
|
audio_bytes = _download_audio_bytes(URL_EN)
|
|
wav, sr = _read_wav_from_bytes(audio_bytes)
|
|
|
|
results = aligner.align(
|
|
audio=(wav, sr),
|
|
text=TEXT_EN,
|
|
language="English",
|
|
)
|
|
assert len(results) == 1
|
|
assert len(results[0]) > 0
|
|
_print_result("single-numpy-tuple-from-request", results)
|
|
|
|
|
|
def test_batch_mixed_inputs(aligner: Qwen3ForcedAligner) -> None:
|
|
"""
|
|
Batch alignment mixing URL, base64, and (np.ndarray, sr) inputs.
|
|
"""
|
|
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 = aligner.align(
|
|
audio=[URL_ZH, zh_b64, (en_wav, en_sr)],
|
|
text=[TEXT_ZH, TEXT_ZH, TEXT_EN],
|
|
language=["Chinese", "Chinese", "English"],
|
|
)
|
|
assert len(results) == 3
|
|
assert all(len(r) > 0 for r in results)
|
|
_print_result("batch-mixed-inputs", results)
|
|
|
|
|
|
def main() -> None:
|
|
aligner = Qwen3ForcedAligner.from_pretrained(
|
|
MODEL_PATH,
|
|
dtype=torch.bfloat16,
|
|
device_map="cuda:0",
|
|
# attn_implementation="flash_attention_2",
|
|
)
|
|
|
|
test_single_url(aligner)
|
|
test_batch_url(aligner)
|
|
test_base64_data_url(aligner)
|
|
test_numpy_tuple_from_request(aligner)
|
|
test_batch_mixed_inputs(aligner)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |