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import streamlit as st
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import torchaudio
import os
import re
import jieba
from difflib import SequenceMatcher
# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load Whisper model for transcription with improved parameters
MODEL_NAME = "alvanlii/whisper-small-cantonese"
language = "zh"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=60,
device=device,
generate_kwargs={
"no_repeat_ngram_size": 4,
"repetition_penalty": 1.15,
"temperature": 0.5,
"top_p": 0.97,
"top_k": 40,
"max_new_tokens": 300,
"do_sample": True
}
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=language, task="transcribe")
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 i, sentence in enumerate(sentences):
if i == 0 or not is_similar(sentence.strip(), cleaned_sentences[-1].strip()):
cleaned_sentences.append(sentence.strip())
return " ".join(cleaned_sentences)
def transcribe_audio(audio_path):
waveform, sample_rate = torchaudio.load(audio_path)
duration = waveform.shape[1] / sample_rate
if duration > 60:
results = []
for start in range(0, int(duration), 55):
end = min(start + 60, int(duration))
chunk = waveform[:, start * sample_rate:end * sample_rate]
if chunk.shape[1] == 0:
continue
temp_filename = f"temp_chunk_{start}.wav"
torchaudio.save(temp_filename, chunk, sample_rate)
if os.path.exists(temp_filename):
try:
result = pipe(temp_filename)["text"]
results.append(result)
finally:
os.remove(temp_filename)
return remove_repeated_phrases(" ".join(results))
return remove_repeated_phrases(pipe(audio_path)["text"])
# Load translation model
tokenizer = AutoTokenizer.from_pretrained("botisan-ai/mt5-translate-yue-zh")
model = AutoModelForSeq2SeqLM.from_pretrained("botisan-ai/mt5-translate-yue-zh").to(device)
def translate(text):
sentences = [s for s in re.split(r'(?<=[。！？])', text) if s]
translations = []
for sentence in sentences:
inputs = tokenizer(sentence, return_tensors="pt").to(device)
outputs = model.generate(inputs["input_ids"], max_length=1000, num_beams=5)
translations.append(tokenizer.decode(outputs[0], skip_special_tokens=True))
return " ".join(translations)
# Streamlit UI
st.set_page_config(page_title="Cantonese Speech Processing", layout="wide")
st.title("🎤 Cantonese Audio Transcription & Translation")
st.write("Upload an audio file to transcribe, translate, and analyze quality.")
uploaded_file = st.file_uploader("Upload your audio file (WAV format)", type=["wav"])
if uploaded_file is not None:
with st.spinner("Processing audio..."):
audio_path = "temp_audio.wav"
with open(audio_path, "wb") as f:
f.write(uploaded_file.read())
transcript = transcribe_audio(audio_path)
st.subheader("📝 Transcription")
st.text_area("Transcript", transcript, height=150)
translated_text = translate(transcript)
st.subheader("🌍 Translation")
st.text_area("Translated Text", translated_text, height=150)
st.success("Processing complete!")
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