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AI Large Model/Step Audio

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tts_app.py 7.20 KB
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mabuyun 提交于 3个月前 . delete wav lfs (#144)
import gradio as gr
import argparse
import torchaudio
from tts import StepAudioTTS
from tokenizer import StepAudioTokenizer
from datetime import datetime
import os
# 保存音频
def save_audio(audio_type, audio_data, sr):
current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
save_path = os.path.join(args.tmp_dir, audio_type, f"{current_time}.wav")
os.makedirs(os.path.dirname(save_path), exist_ok=True)
torchaudio.save(save_path, audio_data, sr)
return save_path
# 普通语音合成
def tts_common(text, speaker, emotion, language, speed):
text = (
(f"({emotion})" if emotion else "")
+ (f"({language})" if language else "")
+ (f"({speed})" if speed else "")
+ text
)
output_audio, sr = tts_engine(text, speaker)
audio_type = "common"
common_path = save_audio(audio_type, output_audio, sr)
return common_path
# RAP / 哼唱模式
def tts_music(text_input_rap, speaker, mode_input):
text_input_rap = f"({mode_input})" + text_input_rap
output_audio, sr = tts_engine(text_input_rap, speaker)
audio_type = "music"
music_path = save_audio(audio_type, output_audio, sr)
return music_path
# 语音克隆
def tts_clone(text, wav_file, speaker_prompt, emotion, language, speed):
clone_speaker = {
"wav_path": wav_file,
"speaker": "custom_voice",
"prompt_text": speaker_prompt,
}
clone_text = (
(f"({emotion})" if emotion else "")
+ (f"({language})" if language else "")
+ (f"({speed})" if speed else "")
+ text
)
output_audio, sr = tts_engine(clone_text, "", clone_speaker)
audio_type = "clone"
clone_path = save_audio(audio_type, output_audio, sr)
return clone_path
def launch_demo(args):
# 选项列表
emotion_options = ["高兴1", "高兴2", "生气1", "生气2", "悲伤1", "撒娇1"]
language_options = ["中文", "英文", "韩语", "日语", "四川话", "粤语", "广东话"]
speed_options = ["慢速1", "慢速2", "快速1", "快速2"]
speaker_options = ["Tingting"]
# Gradio 界面
with gr.Blocks() as demo:
gr.Markdown("## 🎙️ Step-Audio-TTS-3B Demo")
# 普通语音合成
with gr.Tab("Common TTS (普通语音合成)"):
text_input = gr.Textbox(
label="Input Text (输入文本)",
)
speaker_input = gr.Dropdown(
speaker_options,
label="Speaker Selection (音色选择)",
)
emotion_input = gr.Dropdown(
emotion_options,
label="Emotion Style (情感风格)",
allow_custom_value=True,
interactive=True,
)
language_input = gr.Dropdown(
language_options,
label="Language/Dialect (语言/方言)",
allow_custom_value=True,
interactive=True,
)
speed_input = gr.Dropdown(
speed_options,
label="Speech Rate (语速调节)",
allow_custom_value=True,
interactive=True,
)
submit_btn = gr.Button("🔊 Generate Speech (生成语音)")
output_audio = gr.Audio(
label="Output Audio (合成语音)",
interactive=False,
)
submit_btn.click(
tts_common,
inputs=[
text_input,
speaker_input,
emotion_input,
language_input,
speed_input,
],
outputs=output_audio,
)
# RAP / 哼唱模式
with gr.Tab("RAP/Humming Mode (RAP/哼唱模式)"):
text_input_rap = gr.Textbox(
label="Lyrics Input (歌词输入)",
)
speaker_input = gr.Dropdown(
speaker_options,
label="Speaker Selection (音色选择)",
)
mode_input = gr.Radio(
["RAP", "Humming (哼唱)"],
value="RAP",
label="Generation Mode (生成模式)",
)
submit_btn_rap = gr.Button("🎤 Generate Performance (生成演绎)")
output_audio_rap = gr.Audio(
label="Performance Audio (演绎音频)", interactive=False
)
submit_btn_rap.click(
tts_music,
inputs=[text_input_rap, speaker_input, mode_input],
outputs=output_audio_rap,
)
with gr.Tab("Voice Clone (语音克隆)"):
text_input_clone = gr.Textbox(
label="Target Text (目标文本)",
placeholder="Text to be synthesized with cloned voice (待克隆语音合成的文本)",
)
audio_input = gr.File(
label="Reference Audio Upload (参考音频上传)",
)
speaker_prompt = gr.Textbox(
label="Exact text from reference audio (输入参考音频的准确文本)",
)
emotion_input = gr.Dropdown(
emotion_options,
label="Emotion Style (情感风格)",
allow_custom_value=True,
interactive=True,
)
language_input = gr.Dropdown(
language_options,
label="Language/Dialect (语言/方言)",
allow_custom_value=True,
interactive=True,
)
speed_input = gr.Dropdown(
speed_options,
label="Speech Rate (语速调节)",
allow_custom_value=True,
interactive=True,
)
submit_btn_clone = gr.Button("🗣️ Synthesize Cloned Speech (合成克隆语音)")
output_audio_clone = gr.Audio(
label="Cloned Speech Output (克隆语音输出)",
interactive=False,
)
submit_btn_clone.click(
tts_clone,
inputs=[
text_input_clone,
audio_input,
speaker_prompt,
emotion_input,
language_input,
speed_input,
],
outputs=output_audio_clone,
)
# 启动 Gradio demo
demo.queue().launch(server_name=args.server_name, server_port=args.server_port)
if __name__ == "__main__":
# 解析命令行参数
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, required=True, help="Model path.")
parser.add_argument(
"--server-name", type=str, default="0.0.0.0", help="Demo server name."
)
parser.add_argument(
"--server-port", type=int, default=7860, help="Demo server port."
)
parser.add_argument("--tmp_dir", type=str, default="/tmp/gradio", help="Save path.")
args = parser.parse_args()
# 使用解析后的命令行参数设置模型路径
model_path = args.model_path
encoder = StepAudioTokenizer(os.path.join(model_path, "Step-Audio-Tokenizer"))
tts_engine = StepAudioTTS(os.path.join(model_path, "Step-Audio-TTS-3B"), encoder)
launch_demo(args)
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https://gitee.com/AI-LargeModel/Step-Audio.git
git@gitee.com:AI-LargeModel/Step-Audio.git
AI-LargeModel
Step-Audio
Step Audio
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