speech_file_path=Path(__file__).parent/"speech1.mp3"# 创建语音 response=client.audio.speech.create(model="tts-1",voice="alloy",input=text_to_speech # 使用读取的文本作为输入)# 将响应流式传输到文件 response.stream_to_file(speech_file_path)print(f"语音文件已生成在:{speech_file_path}") ...
在脚本中调用OpenAI的TTS模型,指定模型类型、声音类型和输入文本,然后将生成的语音保存到文件。 # 调用OpenAI的TTS模型response = client.audio.speech.create( model="tts-1-hd",# 模型选择voice="echo",# 不同语音模式选择input="你好,世界!"# 生成内容选择)# 将生成的语音保存到文件response.stream_to_file(...
client.audio.speech.create( model="tts-1", voice="alloy", input=text ) speech_file_path = Path(path) with open(speech_file_path, 'wb') as file: file.write(response.content) print(f'文件已保存到: {speech_file_path}') # 使用函数 text_to_speech("Today is a wonderful day to build...
步骤3 – 创建首个文本到语音版本 Now it’s time to create your first text-to-speech. Refer to the code below, and replaceYOUR_API_KEY_HEREwith your actual API key. 现在是创建第一个文本到语音的时候了。请参考下面的代码,并将此处的YOUR_API_KEY_HERE替换为您的实际 API 密钥。 curl https://...
from openai import OpenAIclient = OpenAI(# Defaults to os.environ.get("OPENAI_API_KEY"))chat_completion = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role":"user","content":"Hello world"}])通过 Langchain 等框架 from langchain_openai import ChatOpenAIllm = Ch...
不算特别意外，OpenAI 使用了 transformer 作为主要架构，结合 diffusion model，幸好还不是端到端的 autoregressive 模型，否则太吓人了（不过这样一来，transformer 在自然语言处理，图像视频生成，语音合成（最近 amazon 的工作 BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K ...
Hi everyone, this is what my voice sounds like using OpenAI's new text to speech model called Voice Engine. I was able to use just 15 seconds of a video that I made for a class project to be the reference audio source for the voice you hear right now. What do you think?
Hi everyone, this is what my voice sounds like using OpenAI's new text to speech model called Voice Engine. I was able to use just 15 seconds of a video that I made for a class project to be the reference audio source for the voice you hear right now. What do you think? 作为参考...
" file_path = r"谈读书.mp3" # 文件地址 files = {'file':open(file_path, "rb")} query = { "model":"whisper-1", "language":"zh-cn", # 简体汉语 "response_format":"text", } response = requests.post(url=url, data=query,files=files, headers=headers) print(response.text)...
GPT-4o发布之前,ChatGPT的语音模式功能有着好几秒的延迟,这让整个交互体验非常差,这是因为之前的GPT系列的语音功能是好几个模型的拼合,先把声音转录成文本,再用GPT大模型接受后,输出文本,然后再用text to speech模型生成音频,但这其中会损失非常多的信息,比如说语调,语气中的情绪情感,多个说话人的识别,背景的声...