For specific details on the batching and alignment, the effect of VAD, as well as the chosen alignment model, see the preprintpaper. To reduce GPU memory requirements, try any of the following (2. & 3. can affect quality): reduce batch size, e.g.--batch_size 4 ...
语音转文本技术与语音合成技术的结合将使虚拟个性化代表(如虚拟助手、角色)更加真实和个性化。用户可以创建具有特定声音和个性的VPAs来自动回应电话、邮件或其他通信形式。 应用方向:个性化的虚拟助手、角色扮演游戏、教育等。 全球客服领域的发展设想 智能客服语音助手: ...
Harness the power of OpenAI's revolutionary Whisper technology with WhisperBoard, your go-to app for effortless voice recording and accurate transcription. Whether you're a professional, student, or anyone in between, our app turns your spoken words into written text with unmatched precision. Why ...
Common Voice 15和Fleurs数据集是两个语音及语言数据集,它们是用来训练和评估语音识别技术如Whisper等模型的工具 下图显示了按语言划分的large-v3和large-v2模型的性能分解,使用了在Common Voice 15和Fleurs数据集上评估的WER(单词错误率)或CER(字符错误率) 这个图说明对主流语言的错误率比较低。 后面随着版本是升级...
2. Advanced OpenAI Technology: Benefit from superior accuracy in voice-to-text transcription thanks to advanced technology from OpenAI. 3. Versatile Application: Perfect for a range of uses including personal journaling, note-taking, and setting reminders. ...
By the end of this tutorial, you’ll have a fully functional Python app that allows you to record audio on the fly and automatically transcribes it, making the task of voice-to-text conversion as easy as pressing a button. So, if you’re ready to dive into the world of speech-to-te...
voice_service.py voice_service.py文件包含两个类: Voice抽象类,定义了voiceToText和textToVoice两个方法。 PyttsVoice类,继承Voice类,实现了textToVoice方法,使用pyttsx3库将文本转换为语音。 requirements.txt requirements.txt文件列出了项目所需的所有依赖项。
Common Voice 15和Fleurs数据集是两个语音及语言数据集,它们是用来训练和评估语音识别技术如Whisper等模型的工具 下图显示了按语言划分的large-v3和large-v2模型的性能分解,使用了在Common Voice 15和Fleurs数据集上评估的WER(单词错误率)或CER(字符错误率) ...
Common Voice 15和Fleurs数据集是两个语音及语言数据集,它们是用来训练和评估语音识别技术如Whisper等模型的工具 下图显示了按语言划分的large-v3和large-v2模型的性能分解,使用了在Common Voice 15和Fleurs数据集上评估的WER(单词错误率)或CER(字符错误率) ...
print ( str (transcribed_text)) #Result: Je souhaitechanger mon adresse. 基准方法 在此基准测试中,我们将重点比较模型的大型版本(Whisper 的大型 v2),并将采用以下方法: 采用的指标 对于每个模型,我们将根据多个数据集计算其字错误率 (WER) 和字符错误率 (CER)。此外,我们将测量处理音频文件所需的时间。