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()