defspeech2text(speech_file):transcriber=pipeline(task="automatic-speech-recognition",model="openai/whisper-medium")text_dict=transcriber(speech_file)returntext_dictimportargparseimportjson defmain():parser=argparse.ArgumentParser(description="语音转文本")parser.add_argument("--audio","-a",type=str,hel...
(task="automatic-speech-recognition", model="openai/whisper-medium") text_dict = transcriber(speech_file) return text_dict import argparse import json def main(): parser = argparse.ArgumentParser(description="语音转文本") parser.add_argument("--audio","-a", type=str, help="输出音频文件路径...
推理函数仅需2行,非常简单,基于pipeline实例化1个模型对象,将要转换的音频文件传至模型对象中即可: def speech2text(speech_file):transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-medium")text_dict = transcriber(speech_file)return text_dict 3.4 完整代码 运行完整代码: pytho...
随着人工智能技术的飞速发展,语音转文本(Speech-to-Text, STT)技术已经成为众多应用场景中的关键一环。OpenAI近期推出的Whisper模型,以其强大的多语言支持和高效能,在语音识别领域引起了广泛关注。本文将带您深入了解Whisper模型的技术原理、应用场景,并通过实战操作展示其使用方法。 Whisper模型简介 Whisper是OpenAI研发并...
语音合成工具 WhisperText To Speech 的声音效果如何？一. what is whisper ?Whisper是由OpenAI开发的...
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...
Whisper.net. Speech to text made simple using Whisper Models 模型下载地址:https:///sandrohanea/whisper.net/tree/main/classic 效果 输出信息 whisper_init_from_file_no_state: loading model from 'ggml-small.bin' whisper_model_load: loading model ...
坦白讲,Whisper 的 API 价格非常便宜了,几乎只是 Google Speech2Text API 的四分之一。但是,如果我们假设有 5 门课程,每堂课长 3小时,每周有一次课,那么每个月的转写成本 = 5 x 3 x 4 x 2.7 = 162 元,这个价格还是有点肉疼。 本地转写的话倒是没有上述两个问题,但本地转写的麻烦之处在于:...
(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...
importlibrosaimportnumpyasnp# 加载语音文件audio,sr=librosa.load('speech.wav',sr=None)# 将语音文件转换为时频图spectrogram=librosa.feature.melspectrogram(audio,sr=sr)# 对时频图进行归一化处理normalized_spectrogram=(spectrogram-np.mean(spectrogram))/np.std(spectrogram) ...