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import os
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import soundfile as sf
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from src.SpeechBase.asr import ASR
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from src.SpeechBase.nlp import NLP
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from src.SpeechBase.tts import TTS
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from paddlespeech.cli.asr.infer import ASRExecutor
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class Robot:
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def __init__(self,
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asr_config,
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tts_config,
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asr_init_path,
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ie_model_path=None) -> None:
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self.nlp = NLP(ie_model_path=ie_model_path)
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self.asr = ASR(config_path=asr_config)
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self.tts = TTS(config_path=tts_config)
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self.tts_sample_rate = 24000
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self.asr_sample_rate = 16000
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# 流式识别效果不如端到端的模型，这里流式模型与端到端模型分开
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self.asr_model = ASRExecutor()
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self.asr_name = "conformer_wenetspeech"
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self.warm_up_asrmodel(asr_init_path)
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def warm_up_asrmodel(self, asr_init_path):
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if not os.path.exists(asr_init_path):
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path_dir = os.path.dirname(asr_init_path)
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if not os.path.exists(path_dir):
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os.makedirs(path_dir, exist_ok=True)
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# TTS生成，采样率24000
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text = "生成初始音频"
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self.text2speech(text, asr_init_path)
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# asr model初始化
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self.asr_model(
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asr_init_path,
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model=self.asr_name,
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lang='zh',
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sample_rate=16000,
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force_yes=True)
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def speech2text(self, audio_file):
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self.asr_model.preprocess(self.asr_name, audio_file)
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self.asr_model.infer(self.asr_name)
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res = self.asr_model.postprocess()
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return res
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def text2speech(self, text, outpath):
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wav = self.tts.offlineTTS(text)
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sf.write(outpath, wav, samplerate=self.tts_sample_rate)
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res = wav
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return res
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def text2speechStream(self, text):
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for sub_wav_base64 in self.tts.streamTTS(text=text):
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yield sub_wav_base64
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def text2speechStreamBytes(self, text):
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for wav_bytes in self.tts.streamTTSBytes(text=text):
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yield wav_bytes
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def chat(self, text):
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result = self.nlp.chat(text)
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return result
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def ie(self, text):
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result = self.nlp.ie(text)
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return result
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