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import io
import os
from pathlib import Path
from tempfile import NamedTemporaryFile
import time
import gradio as gr
from openai import OpenAI
from pydub import AudioSegment
from dotenv import load_dotenv
# 加載環境變量
load_dotenv()
# 獲取 OpenAI API Key (如果在環境變量中設置了)
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
# 标准音频模型和声音选项
STANDARD_AUDIO_MODELS = [
"tts-1",
"tts-1-hd",
]
STANDARD_VOICES = [
"alloy",
"echo",
"fable",
"onyx",
"nova",
"shimmer",
]
# 优化脚本处理 - 合并相同说话者连续文本
def optimize_script(script):
lines = [line.strip() for line in script.splitlines() if line.strip()]
optimized = []
current_speaker = None
current_text = ""
for line in lines:
if line.lower().startswith("speaker-1:"):
speaker = "speaker-1"
text = line.split(":", 1)[1].strip()
elif line.lower().startswith("speaker-2:"):
speaker = "speaker-2"
text = line.split(":", 1)[1].strip()
else:
speaker = "speaker-1" # 默认使用说话者1
text = line
# 如果说话者变了,保存之前的文本并开始新的
if speaker != current_speaker and current_text:
optimized.append((current_speaker, current_text))
current_text = text
current_speaker = speaker
else:
# 相同说话者,合并文本(加空格)
if current_text:
current_text += " " + text
else:
current_text = text
current_speaker = speaker
# 添加最后一个说话者的文本
if current_text:
optimized.append((current_speaker, current_text))
return optimized
def get_mp3(text: str, voice: str, audio_model: str, audio_api_key: str) -> bytes:
"""使用 OpenAI TTS API 生成音频"""
# 检查文本长度,OpenAI TTS API 有 4096 个标记的限制
# 大约 1000 个汉字约等于 2000-3000 个标记,为安全起见,我们将限制设为 1000 个字符
MAX_TEXT_LENGTH = 1000
client = OpenAI(api_key=audio_api_key)
# 如果文本长度超过限制,分割文本
if len(text) > MAX_TEXT_LENGTH:
print(f"Text too long ({len(text)} chars), splitting into chunks")
# 将文本分割成更小的块
text_chunks = []
for i in range(0, len(text), MAX_TEXT_LENGTH):
text_chunks.append(text[i:i + MAX_TEXT_LENGTH])
# 为每个块生成音频并合并
combined_audio = b""
for chunk in text_chunks:
try:
with client.audio.speech.with_streaming_response.create(
model=audio_model,
voice=voice,
input=chunk,
) as response:
with io.BytesIO() as file:
for audio_chunk in response.iter_bytes():
file.write(audio_chunk)
combined_audio += file.getvalue()
except Exception as e:
print(f"Error generating audio for chunk: {e}")
raise
return combined_audio
else:
# 原始逻辑,处理短文本
try:
with client.audio.speech.with_streaming_response.create(
model=audio_model,
voice=voice,
input=text,
) as response:
with io.BytesIO() as file:
for chunk in response.iter_bytes():
file.write(chunk)
return file.getvalue()
except Exception as e:
print(f"Error generating audio: {e}")
raise
def generate_audio_from_script(
script: str,
audio_api_key: str,
audio_model: str = "tts-1",
speaker1_voice: str = "onyx",
speaker2_voice: str = "nova",
volume_boost: float = 0,
) -> tuple[bytes, str]:
"""从脚本生成音频,支持两个说话者,并优化 API 调用"""
status_log = []
# 优化脚本处理
optimized_script = optimize_script(script)
# 使用 pydub 处理音频合并
combined_segment = None
# 处理每一段
for speaker, text in optimized_script:
voice_to_use = speaker1_voice if speaker == "speaker-1" else speaker2_voice
status_log.append(f"[{speaker}] {text}")
try:
# 生成这一段的音频
audio_chunk = get_mp3(
text,
voice_to_use,
audio_model,
audio_api_key
)
# 将二进制数据转换为 AudioSegment
with NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
temp_file.write(audio_chunk)
temp_file_path = temp_file.name
# 读取音频
chunk_segment = AudioSegment.from_mp3(temp_file_path)
# 删除临时文件
os.unlink(temp_file_path)
# 合并音频段
if combined_segment is None:
combined_segment = chunk_segment
else:
combined_segment += chunk_segment
except Exception as e:
status_log.append(f"[错误] 无法生成音频: {str(e)}")
# 如果沒有生成任何音頻段
if combined_segment is None:
status_log.append("[錯誤] 沒有生成任何音頻")
return b"", "\n".join(status_log)
# 如果需要調整音量
if volume_boost > 0:
try:
# 調整音量
combined_segment = combined_segment + volume_boost # 增加音量 (dB)
status_log.append(f"[音量] 已增加 {volume_boost} dB")
except Exception as e:
status_log.append(f"[警告] 音量調整失敗: {str(e)}")
# 將 AudioSegment 轉換為二進制數據
output = io.BytesIO()
combined_segment.export(output, format="mp3")
combined_audio = output.getvalue()
return combined_audio, "\n".join(status_log)
def save_audio_file(audio_data: bytes) -> str:
"""将音频数据保存为临时文件"""
temp_dir = Path("./temp_audio")
temp_dir.mkdir(exist_ok=True)
# 清理旧文件
for old_file in temp_dir.glob("*.mp3"):
if old_file.stat().st_mtime < (time.time() - 24*60*60): # 24小时前的文件
old_file.unlink()
# 创建新的临时文件
temp_file = NamedTemporaryFile(
dir=temp_dir,
delete=False,
suffix=".mp3"
)
temp_file.write(audio_data)
temp_file.close()
return temp_file.name
def process_and_save_audio(script, api_key, model, voice1, voice2, volume_boost):
"""处理音频生成并保存文件"""
try:
audio_data, status_log = generate_audio_from_script(
script,
api_key,
model,
voice1,
voice2,
volume_boost
)
audio_path = save_audio_file(audio_data)
return audio_path, status_log
except Exception as e:
error_message = f"生成音频时发生错误: {str(e)}"
print(error_message)
return None, error_message
# Gradio 界面
def create_gradio_interface():
with gr.Blocks(title="TTS Generator", css="""
#header { text-align: center; margin-bottom: 20px; }
""") as demo:
gr.Markdown("# 语音合成器 | TTS Generator", elem_id="header")
with gr.Row():
with gr.Column(scale=1):
# 输入区
script_input = gr.Textbox(
label="输入脚本 | Input Script",
placeholder="""请粘贴脚本内容,格式如下:
speaker-1: 欢迎来到 David888 Podcast,我是 David...
speaker-2: 大家好,我是 Cordelia...
没有标记说话者的行会默认使用说话者1的声音。
提示:为提高效率,相同说话者的多行文字将自动合并处理。""",
lines=20
)
api_key = gr.Textbox(
label="OpenAI API Key",
type="password"
)
with gr.Row():
audio_model = gr.Dropdown(
label="音频模型 | Audio Model",
choices=STANDARD_AUDIO_MODELS,
value="tts-1"
)
speaker1_voice = gr.Dropdown(
label="说话者1声音 | Speaker 1 Voice",
choices=STANDARD_VOICES,
value="onyx"
)
speaker2_voice = gr.Dropdown(
label="说话者2声音 | Speaker 2 Voice",
choices=STANDARD_VOICES,
value="nova"
)
volume_boost = gr.Slider(
label="音量增益 (dB) | Volume Boost (dB)",
minimum=0,
maximum=20,
value=6,
step=1,
info="增加音頻音量,單位為分貝(dB)。建議值:6-10 dB"
)
generate_button = gr.Button("生成音频 | Generate Audio")
with gr.Column(scale=1):
# 输出区
audio_output = gr.Audio(
label="生成的音频 | Generated Audio",
type="filepath"
)
status_output = gr.Textbox(
label="生成状态 | Generation Status",
lines=20,
show_copy_button=True
)
# 事件处理
generate_button.click(
fn=process_and_save_audio,
inputs=[
script_input,
api_key,
audio_model,
speaker1_voice,
speaker2_voice,
volume_boost
],
outputs=[audio_output, status_output]
)
return demo
if __name__ == "__main__":
demo = create_gradio_interface()
demo.launch() |