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import os
import torch
import torchaudio
import numpy as np
import re
import streamlit as st
from difflib import SequenceMatcher
from transformers import pipeline
# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load Whisper model for transcription
MODEL_NAME = "alvanlii/whisper-small-cantonese"
language = "zh"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
generate_kwargs={
"no_repeat_ngram_size": 3,
"repetition_penalty": 1.3,
"temperature": 0.7,
"top_p": 0.9,
"top_k": 50
}
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=language, task="transcribe")
# Load quality rating model
rating_pipe = pipeline("text-classification", model="Leo0129/CustomModel-multilingual-sentiment-analysis", device=device)
def is_similar(a, b, threshold=0.8):
return SequenceMatcher(None, a, b).ratio() > threshold
def remove_repeated_phrases(text):
sentences = re.split(r'(?<=[。！？])', text)
cleaned_sentences = []
for sentence in sentences:
if not cleaned_sentences or not is_similar(sentence.strip(), cleaned_sentences[-1].strip()):
cleaned_sentences.append(sentence.strip())
return " ".join(cleaned_sentences)
def remove_punctuation(text):
return re.sub(r'[^\w\s]', '', text)
def transcribe_audio(audio_path):
waveform, sample_rate = torchaudio.load(audio_path)
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
waveform = waveform.squeeze(0).numpy()
duration = waveform.shape[0] / sample_rate
if duration > 60:
chunk_size = sample_rate * 55
step_size = sample_rate * 50
results = []
for start in range(0, waveform.shape[0], step_size):
chunk = waveform[start:start + chunk_size]
if chunk.shape[0] == 0:
break
transcript = pipe({"sampling_rate": sample_rate, "raw": chunk})["text"]
results.append(remove_punctuation(transcript))
return remove_punctuation(remove_repeated_phrases(" ".join(results)))
return remove_punctuation(remove_repeated_phrases(pipe({"sampling_rate": sample_rate, "raw": waveform})["text"]))
def rate_quality(text):
chunks = [text[i:i+512] for i in range(0, len(text), 512)]
results = rating_pipe(chunks, batch_size=4)
label_map = {"Very Negative": "Very Poor", "Negative": "Poor", "Neutral": "Neutral", "Positive": "Good", "Very Positive": "Very Good"}
processed_results = [label_map.get(res["label"], "Unknown") for res in results]
return max(set(processed_results), key=processed_results.count)
# Streamlit UI
st.title("Audio Transcription and Quality Rating")
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "flac"])
if uploaded_file is not None:
st.audio(uploaded_file, format="audio/wav")
temp_audio_path = "temp_audio.wav"
with open(temp_audio_path, "wb") as f:
f.write(uploaded_file.read())
st.write("Processing audio...")
transcript = transcribe_audio(temp_audio_path)
st.subheader("Transcript")
st.write(transcript)
quality_rating = rate_quality(transcript)
st.subheader("Quality Rating")
st.write(quality_rating)
os.remove(temp_audio_path)
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