openAI-whisper-SpeechToText A speech-to-text model is a type of artificial intelligence model designed to convert spoken language or audio input into written text. This technology is commonly used in applications like transcription services, voice assistants, and accessibility tools for individuals with...
Node.js plugin for speech recognition that works with OpenAI's Whisper models using ONNX. - Alexandr-Janashvili/whisper-onnx-speech-to-text
disposeModel(): dispose initialized model. Made with Transformers.js ShellJS npm iwhisper-onnx-speech-to-text Repository github.com/Alexandr-Janashvili/whisper-onnx-speech-to-text Weekly Downloads Tryon RunKit Reportmalware
Frequently Asked Questions (FAQs) about Speech-to-Text with Whisper, React, and Node In this article, we’ll build a speech-to-text application using OpenAI’s Whisper, along with React, Node.js, and FFmpeg. The app will take user input, synthesize it into speech using OpenAI’s Whisper...
importwhispermodel=whisper.load_model("base")result=model.transcribe("I_Have_A_Dream_Speech.mp3",,fp16="False")print(result["text"]) 这个脚本可以直接输出音频转出的文字。 利用Python脚本,可以将结果直接储存在变量当中,便于进行后续处理。 对于比较长的音频,需要运行很长时间,特别大的音频可能无法导入,...
这是一个通过反向操作Whisper构建的开源文本转语音系统。在GitHub上有一个名为collabora/WhisperSpeech的项目,它是一个开源的文本转语音系统,也是通过反向操作Whisper构建的。
print(french_to_english["text"]) task=’translate’means that we are performing a translation task. Below is the final result. I was asked to make a speech. I'm going to tell you right away, ladies and gentlemen, that I'm going to speak without saying anything. ...
whisper官网,github,openai推出的自动语音辨识模型 什么是whisper? Whisper是OpenAI提供的一种自动语音识别(Automatic Speech Recognition,ASR)系统。它是基于深度学习技术和大规模语音数据集训练而成的模型,用于将语音转换为文本。Whisper的目标是提供准确、高质量的语音识别功能,使用户能够更轻松地处理语音数据并获取相关信息...
whisper官网,openai推出的自动语音辨识模型,github下载 什么是whisper? Whisper是OpenAI提供的一种自动语音识别(Automatic Speech Recognition,ASR)系统。它是基于深度学习技术和大规模语音数据集训练而成的模型,用于将语音转换为文本。Whisper的目标是提供准确、高质量的语音识别功能,使用户能够更轻松地处理语音数据并获取相关...
(sampling_rate=sampling_rate))#获取第一个音频文件并将其转录input_speech=dataset[0]['audio']input_features=process(input_speech["array"],Sample_rate=input_speech["sampling_rate"],return_tensors="pt").input_features.to(device)predicted_ids=model.generate(input_features,forced_decoder_ids=forced...