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import whisper
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from chatbot import Chatbot
from utils.models_and_path import WHISPER_MODEL_NAME
class WhisperChatbot(Chatbot):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.whisper_model = whisper.load_model(WHISPER_MODEL_NAME)
self._load_translation_engine()
def response(self, audio):
self._clean_audio()
self._load_audio(audio)
self._process_audio()
en_result = super().response(self.text)
if self.lang != "en":
result_translated = self._translate_text(text=en_result, source="en", target=self.lang)['text']
else:
result_translated = en_result
return self.transcribed_text, self.text, self.lang, en_result, result_translated
def _load_translation_engine(self):
self.translation_prompt = PromptTemplate(
input_variables=["source", "target", "text"],
template="Translate from language {source} to {target}: {text}?",
)
self.translation_chain = LLMChain(llm=self.LLM, prompt=self.translation_prompt)
def _load_audio(self, audio):
# assert isinstance(audio, bytes), "Audio must be bytes"
# assert self.whisper_model, "Whisper model not loaded"
# load audio and pad/trim it to fit 30 seconds
self.audio = whisper.pad_or_trim(
whisper.load_audio(audio)
)
def _process_audio(self):
# assert self.audio, "Audio not loaded"
# assert self.whisper_model, "Whisper model not loaded"
# Make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(self.audio).to(self.whisper_model.device)
# Detcet language
_, probas = self.whisper_model.detect_language(mel)
self.lang = max(probas, key=probas.get)
# Decode the audio
options = whisper.DecodingOptions(fp16=False)
self.transcribed_text = whisper.decode(self.whisper_model, mel, options).text
# Check the language of the audio;
# if it's english, use the transcribed text as is
# else, translate it to english
if self.lang == "en":
self.text = self.transcribed_text
else:
# translate from detected lang to en
self.text = self._translate_text(
text=self.transcribed_text,
source=self.lang,
target="en"
)['text']
def _translate_text(self, text, source, target):
return self.translation_chain({
"source": source,
"target": target,
"text": text
})
def _clean_audio(self):
self.audio = None
self.lang = None
self.text = None
self.transcribed_text = None |