212 lines
7.5 KiB
Python
212 lines
7.5 KiB
Python
"""
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优化后的数字人生成API
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按照新的文件管理架构设计
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"""
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import requests
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import os
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import time
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import logging
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from typing import Dict, Any
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from api import Config, APIException, _make_request, _copy_file
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logger = logging.getLogger(__name__)
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def generate_digital_human_v2(speech_text: str, sample_video: str, sample_voice: str, uuid: str) -> Dict[str, Any]:
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"""
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生成数字人 (优化版本)
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Args:
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speech_text: 语音文本
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sample_video: 样本视频文件名（在上传目录中）
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sample_voice: 样本语音文件名（在上传目录中）
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uuid: 唯一标识符
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Returns:
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生成结果
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Raises:
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APIException: 生成失败时抛出异常
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"""
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logger.info(f"Generating digital human for UUID: {uuid}")
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try:
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from file_upload import file_manager
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# 步骤1: 将音频文件复制到TTS服务目录进行预处理
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tts_audio_filename = file_manager.copy_audio_for_tts(sample_voice, f"dh_{uuid}")
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# 步骤2: 预处理和训练语音模型
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url = f"{Config.VOICE_SERVICE_URL}/v1/preprocess_and_tran"
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request_body_one = {
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"format": "wav",
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"reference_audio": tts_audio_filename, # 使用TTS目录中的文件名
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"lang": "zh"
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}
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response_one = _make_request(url, request_body_one)
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# 检查响应中是否包含必要字段
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if 'asr_format_audio_url' not in response_one:
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logger.error(f"Voice preprocessing response missing asr_format_audio_url: {response_one}")
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raise APIException(f"Voice preprocessing failed: missing asr_format_audio_url in response", 500)
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asr_format_audio_url = response_one['asr_format_audio_url']
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reference_audio_text = response_one.get('reference_audio_text', '')
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# 步骤3: 生成语音
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url = f"{Config.VOICE_SERVICE_URL}/v1/invoke"
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request_body_two = {
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"speaker": uuid,
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"text": speech_text,
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"reference_audio": asr_format_audio_url,
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"reference_text": reference_audio_text,
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**Config.DEFAULT_VOICE_PARAMS
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}
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response = requests.post(url, json=request_body_two, timeout=60)
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logger.info(f"Voice generation response status: {response.status_code}")
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if response.status_code != 200:
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logger.error(f"Voice generation failed: {response.text}")
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raise APIException(f"Voice generation failed: {response.text}", response.status_code)
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# 保存生成的音频文件到resource目录
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generated_audio_filename = f"{uuid}output.wav"
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generated_audio_path = os.path.join(Config.RESOURCE_DIR, generated_audio_filename)
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with open(generated_audio_path, "wb") as f:
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f.write(response.content)
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logger.info(f"Generated audio saved: {generated_audio_path}")
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# 步骤4: 将视频和生成的音频复制到Face2Face服务目录
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# 首先将生成的音频从resource目录复制到uploads目录（临时）
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upload_audio_dir = "/mnt/docker/resource/uploads/audio"
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os.makedirs(upload_audio_dir, exist_ok=True)
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upload_audio_path = os.path.join(upload_audio_dir, generated_audio_filename)
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_copy_file(generated_audio_path, upload_audio_path)
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face2face_video, face2face_audio = file_manager.copy_files_for_face2face(
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sample_video, generated_audio_filename, uuid
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)
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# 步骤5: 提交视频生成任务
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url = f"{Config.VIDEO_SERVICE_URL}/easy/submit"
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request_body = {
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"audio_url": face2face_audio,
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"video_url": face2face_video,
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"code": str(int(time.time())), # 使用时间戳作为唯一任务ID
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"chaofen": 0,
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"watermark_switch": 0,
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"pn": 1
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}
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result = _make_request(url, request_body)
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logger.info(f"Digital human generation submitted successfully: {result}")
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# 清理临时文件
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try:
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os.remove(upload_audio_path)
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except:
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pass
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return result
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except Exception as e:
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logger.error(f"Failed to generate digital human: {str(e)}")
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if isinstance(e, APIException):
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raise
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raise APIException(f"Failed to generate digital human: {str(e)}", 500)
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def generate_voice_v2(text: str, reference_audio: str, reference_text: str, uuid: str) -> str:
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"""
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生成语音 (优化版本)
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Args:
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text: 要转换的文本
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reference_audio: 参考音频文件名（在上传目录中）
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reference_text: 参考文本
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uuid: 唯一标识符
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Returns:
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生成的音频文件路径（在resource目录中）
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Raises:
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APIException: 生成失败时抛出异常
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"""
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logger.info(f"Generating voice for UUID: {uuid}")
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try:
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from file_upload import file_manager
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# 先将参考音频复制到TTS服务目录
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tts_audio_filename = file_manager.copy_audio_for_tts(reference_audio, uuid)
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url = f"{Config.VOICE_SERVICE_URL}/v1/invoke"
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request_body = {
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"speaker": uuid,
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"text": text,
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"reference_audio": tts_audio_filename, # 使用TTS目录中的文件名
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"reference_text": reference_text,
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**Config.DEFAULT_VOICE_PARAMS
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}
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response = requests.post(url, json=request_body, timeout=60)
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logger.info(f"Voice generation response status: {response.status_code}")
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if response.status_code == 200:
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# 保存生成的音频到resource目录
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output_filename = f"{uuid}output.wav"
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output_path = os.path.join(Config.RESOURCE_DIR, output_filename)
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with open(output_path, "wb") as f:
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f.write(response.content)
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logger.info(f"Generated voice saved to: {output_path}")
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return output_path
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else:
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logger.error(f"Voice generation failed: {response.text}")
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raise APIException(f"Voice generation failed: {response.text}", response.status_code)
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except Exception as e:
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if isinstance(e, APIException):
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raise
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logger.error(f"Network error during voice generation: {str(e)}")
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raise APIException(f"Voice generation error: {str(e)}", 500)
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def train_voice_v2(voice_file_name: str) -> Dict[str, Any]:
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"""
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训练语音模型 (优化版本)
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Args:
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voice_file_name: 语音文件名（在上传目录中）
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Returns:
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训练结果
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Raises:
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APIException: 训练失败时抛出异常
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"""
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logger.info(f"Training voice model with file: {voice_file_name}")
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try:
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from file_upload import file_manager
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# 将音频文件复制到TTS服务目录
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temp_uuid = "train_" + str(int(time.time()))
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tts_audio_filename = file_manager.copy_audio_for_tts(voice_file_name, temp_uuid)
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url = f"{Config.VOICE_SERVICE_URL}/v1/preprocess_and_tran"
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request_body = {
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"format": "wav",
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"reference_audio": tts_audio_filename, # 使用TTS目录中的文件名
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"lang": "zh"
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}
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return _make_request(url, request_body)
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except Exception as e:
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if isinstance(e, APIException):
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raise
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logger.error(f"Voice training error: {str(e)}")
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raise APIException(f"Voice training error: {str(e)}", 500)
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