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import numpy as np
import librosa
import soundfile as sf
from datasets import load_dataset
from transformers import pipeline
# Initialize pipelines for speech recognition and tts models
asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en")
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
# Speech-to-Text Function
def transcribe_speech(filepath):
if filepath is None:
return "No audio found. Please retry."
output = asr(filepath)
return output["text"]
# Long-Form Audio Transcription
def transcribe_long_form(filepath):
if filepath is None:
return "No audio found. Please retry."
# Load and preprocess audio
audio, sampling_rate = sf.read(filepath)
audio_transposed = np.transpose(audio)
audio_mono = librosa.to_mono(audio_transposed)
audio_16KHz = librosa.resample(audio_mono, orig_sr=sampling_rate, target_sr=16000)
# Transcribe using ASR pipeline
chunks = asr(audio_16KHz, chunk_length_s=30, batch_size=4, return_timestamps=True)["chunks"]
# Combine all transcriptions
return "\n".join([chunk["text"] for chunk in chunks])
# Text-to-Speech Function
def text_to_speech(text):
if not text.strip():
return "No text provided. Please enter text to synthesize."
narrated_text = narrator(text)
audio_array = narrated_text["audio"][0].flatten() # Flatten the 2D array to 1D
sampling_rate = narrated_text["sampling_rate"] # Get sampling rate
return sampling_rate, audio_array
# Sample Dataset Access Function
def get_dataset_sample(idx):
dataset = load_dataset("librispeech_asr", split="train.clean.100", streaming=True, trust_remote_code=True)
# example = next(iter(dataset))
dataset_head = list(dataset.take(5))
sample = dataset_head[idx]
audio_array = sample["audio"]["array"]
sampling_rate = sample["audio"]["sampling_rate"]
transcription = sample["text"]
return (audio_array, sampling_rate), transcription
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