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import io
import librosa
import numpy as np
from typing import Optional
from .config import pipe
TASK = "transcribe"
BATCH_SIZE = 8
class A2T:
def __init__(self, mic):
self.mic = mic
def __generate_text(self, inputs, task: Optional[str] = None) -> str:
if inputs is None:
raise ValueError(f"Input audio is None {inputs}, please provide audio")
transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
return transcribed_text
@staticmethod
def __preprocess(raw: bytes) -> np.ndarray:
print(f"Raw type: {type(raw)}")
if not isinstance(raw, bytes):
raise ValueError("Expected raw audio data as bytes")
try:
chunk = io.BytesIO(raw)
print(f"Chunk type: {type(chunk)}")
audio, sample_rate = librosa.load(chunk, sr=16000)
print(f"Sample rate : {sample_rate}")
return audio
except Exception as e:
print(f"Error loading audio in the preprocess function in the A2T class: {e}")
def predict(self) -> str:
try:
if self.mic is not None:
raw = self.mic
audio = self.__preprocess(raw=raw)
print(f"audio type : {type(audio)} \n shape : {audio.shape} \n audio max value : {np.max(audio)}")
else:
raise ValueError(f"Please provide audio your audio {self.mic}")
if isinstance(audio, np.ndarray):
return self.__generate_text(inputs=audio, task=TASK)
else:
raise ValueError("Audio is not np array")
except Exception as e:
print(f"An error occurred in the predict function in the A2T class: {e}") |