193 lines
6.3 KiB
Python
193 lines
6.3 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.
|
|
import os
|
|
import time
|
|
import torch
|
|
import soundfile as sf
|
|
|
|
from qwen_tts import Qwen3TTSModel
|
|
|
|
|
|
def ensure_dir(d: str):
|
|
os.makedirs(d, exist_ok=True)
|
|
|
|
|
|
def run_case(tts: Qwen3TTSModel, out_dir: str, case_name: str, call_fn):
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
|
|
wavs, sr = call_fn()
|
|
|
|
torch.cuda.synchronize()
|
|
t1 = time.time()
|
|
print(f"[{case_name}] time: {t1 - t0:.3f}s, n_wavs={len(wavs)}, sr={sr}")
|
|
|
|
for i, w in enumerate(wavs):
|
|
sf.write(os.path.join(out_dir, f"{case_name}_{i}.wav"), w, sr)
|
|
|
|
|
|
def main():
|
|
device = "cuda:0"
|
|
MODEL_PATH = "Qwen/Qwen3-TTS-12Hz-1.7B-Base/"
|
|
OUT_DIR = "qwen3_tts_test_voice_clone_output_wav"
|
|
ensure_dir(OUT_DIR)
|
|
|
|
tts = Qwen3TTSModel.from_pretrained(
|
|
MODEL_PATH,
|
|
device_map=device,
|
|
dtype=torch.bfloat16,
|
|
attn_implementation="flash_attention_2",
|
|
)
|
|
|
|
# Reference audio(s)
|
|
ref_audio_path_1 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/clone_2.wav"
|
|
ref_audio_path_2 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/clone_1.wav"
|
|
|
|
ref_audio_single = ref_audio_path_1
|
|
ref_audio_batch = [ref_audio_path_1, ref_audio_path_2]
|
|
|
|
ref_text_single = "Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you."
|
|
ref_text_batch = [
|
|
"Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you.",
|
|
"甚至出现交易几乎停滞的情况。",
|
|
]
|
|
|
|
# Synthesis targets
|
|
syn_text_single = "Good one. Okay, fine, I'm just gonna leave this sock monkey here. Goodbye."
|
|
syn_lang_single = "Auto"
|
|
|
|
syn_text_batch = [
|
|
"Good one. Okay, fine, I'm just gonna leave this sock monkey here. Goodbye.",
|
|
"其实我真的有发现,我是一个特别善于观察别人情绪的人。",
|
|
]
|
|
syn_lang_batch = ["Chinese", "English"]
|
|
|
|
common_gen_kwargs = dict(
|
|
max_new_tokens=2048,
|
|
do_sample=True,
|
|
top_k=50,
|
|
top_p=1.0,
|
|
temperature=0.9,
|
|
repetition_penalty=1.05,
|
|
subtalker_dosample=True,
|
|
subtalker_top_k=50,
|
|
subtalker_top_p=1.0,
|
|
subtalker_temperature=0.9,
|
|
)
|
|
|
|
for xvec_only in [False, True]:
|
|
mode_tag = "xvec_only" if xvec_only else "icl"
|
|
|
|
# Case 1: prompt single + synth single, direct
|
|
run_case(
|
|
tts, OUT_DIR, f"case1_promptSingle_synSingle_direct_{mode_tag}",
|
|
lambda: tts.generate_voice_clone(
|
|
text=syn_text_single,
|
|
language=syn_lang_single,
|
|
ref_audio=ref_audio_single,
|
|
ref_text=ref_text_single,
|
|
x_vector_only_mode=xvec_only,
|
|
**common_gen_kwargs,
|
|
),
|
|
)
|
|
|
|
# Case 1b: prompt single + synth single, via create_voice_clone_prompt
|
|
def _case1b():
|
|
prompt_items = tts.create_voice_clone_prompt(
|
|
ref_audio=ref_audio_single,
|
|
ref_text=ref_text_single,
|
|
x_vector_only_mode=xvec_only,
|
|
)
|
|
return tts.generate_voice_clone(
|
|
text=syn_text_single,
|
|
language=syn_lang_single,
|
|
voice_clone_prompt=prompt_items,
|
|
**common_gen_kwargs,
|
|
)
|
|
|
|
run_case(
|
|
tts, OUT_DIR, f"case1_promptSingle_synSingle_promptThenGen_{mode_tag}",
|
|
_case1b,
|
|
)
|
|
|
|
# Case 2: prompt single + synth batch, direct
|
|
run_case(
|
|
tts, OUT_DIR, f"case2_promptSingle_synBatch_direct_{mode_tag}",
|
|
lambda: tts.generate_voice_clone(
|
|
text=syn_text_batch,
|
|
language=syn_lang_batch,
|
|
ref_audio=ref_audio_single,
|
|
ref_text=ref_text_single,
|
|
x_vector_only_mode=xvec_only,
|
|
**common_gen_kwargs,
|
|
),
|
|
)
|
|
|
|
# Case 2b: prompt single + synth batch, via create_voice_clone_prompt
|
|
def _case2b():
|
|
prompt_items = tts.create_voice_clone_prompt(
|
|
ref_audio=ref_audio_single,
|
|
ref_text=ref_text_single,
|
|
x_vector_only_mode=xvec_only,
|
|
)
|
|
return tts.generate_voice_clone(
|
|
text=syn_text_batch,
|
|
language=syn_lang_batch,
|
|
voice_clone_prompt=prompt_items,
|
|
**common_gen_kwargs,
|
|
)
|
|
|
|
run_case(
|
|
tts, OUT_DIR, f"case2_promptSingle_synBatch_promptThenGen_{mode_tag}",
|
|
_case2b,
|
|
)
|
|
|
|
# Case 3: prompt batch + synth batch, direct
|
|
run_case(
|
|
tts, OUT_DIR, f"case3_promptBatch_synBatch_direct_{mode_tag}",
|
|
lambda: tts.generate_voice_clone(
|
|
text=syn_text_batch,
|
|
language=syn_lang_batch,
|
|
ref_audio=ref_audio_batch,
|
|
ref_text=ref_text_batch,
|
|
x_vector_only_mode=[xvec_only, xvec_only],
|
|
**common_gen_kwargs,
|
|
),
|
|
)
|
|
|
|
# Case 3b: prompt batch + synth batch, via create_voice_clone_prompt
|
|
def _case3b():
|
|
prompt_items = tts.create_voice_clone_prompt(
|
|
ref_audio=ref_audio_batch,
|
|
ref_text=ref_text_batch,
|
|
x_vector_only_mode=[xvec_only, xvec_only],
|
|
)
|
|
return tts.generate_voice_clone(
|
|
text=syn_text_batch,
|
|
language=syn_lang_batch,
|
|
voice_clone_prompt=prompt_items,
|
|
**common_gen_kwargs,
|
|
)
|
|
|
|
run_case(
|
|
tts, OUT_DIR, f"case3_promptBatch_synBatch_promptThenGen_{mode_tag}",
|
|
_case3b,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|