app.py

import streamlit as st
import torch
import torchaudio
import librosa
import numpy as np
import tempfile
from transformers import WhisperForConditionalGeneration, WhisperProcessor
from jiwer import wer
import time
import re

# Title
st.title("🎙️ Bahasa Rojak Transcriber (Whisper Fine-tuned)")

# Session state initialization
if "audio_bytes" not in st.session_state:
    st.session_state.audio_bytes = None
if "audio_path" not in st.session_state:
    st.session_state.audio_path = None
if "ground_truth" not in st.session_state:
    st.session_state.ground_truth = ""
if "predicted_text" not in st.session_state:
    st.session_state.predicted_text = ""
if "wer_value" not in st.session_state:
    st.session_state.wer_value = None
if "selected_tab" not in st.session_state:
    st.session_state.selected_tab = "📁 Upload Audio"
if "previous_tab" not in st.session_state:
    st.session_state.previous_tab = "📁 Upload Audio"

# Tab Selection using st.tabs()
tab1, tab2 = st.tabs(["📁 Upload Audio", "🎤 Record Audio"])

# Reset state if tab is changed
if st.session_state.selected_tab != st.session_state.previous_tab:
    st.session_state.audio_bytes = None
    st.session_state.audio_path = None
    st.session_state.ground_truth = ""
    st.session_state.predicted_text = ""
    st.session_state.wer_value = None
    st.session_state.previous_tab = st.session_state.selected_tab

# Tab 1: Upload Audio
with tab1:
    uploaded_file = st.file_uploader("Upload a .wav or .mp3 file", type=["wav", "mp3", "flac", "m4a", "ogg"])
    if uploaded_file:
        try:
            st.session_state.audio_bytes = uploaded_file.read()
            with tempfile.NamedTemporaryFile(delete=False, suffix=f".{uploaded_file.name.split('.')[-1]}") as tmp:
                tmp.write(st.session_state.audio_bytes)
                st.session_state.audio_path = tmp.name

            librosa.load(st.session_state.audio_path, sr=16000)  # validate
            st.audio(st.session_state.audio_bytes, format="audio/wav")
        except Exception as e:
            st.error(f"❌ Failed to read audio file: {str(e)}")
            st.session_state.audio_bytes = None

# Tab 2: Record Audio
with tab2:
    duration = st.slider("Recording Duration (seconds)", 1, 60, 12)
    if st.button("Start Recording"):
        try:
            st.info(f"Recording for {duration} seconds... Please speak now.")
            recording = sd.rec(int(duration * 16000), samplerate=16000, channels=1, dtype='float32')
            sd.wait()
            recording = np.squeeze(recording)

            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
                torchaudio.save(tmp.name, torch.tensor(recording).unsqueeze(0), 16000)
                st.session_state.audio_path = tmp.name
            librosa.load(st.session_state.audio_path, sr=16000)
            st.audio(recording, format="audio/wav")
        except Exception as e:
            st.error(f"❌ Failed to process recorded audio: {str(e)}")
            st.session_state.audio_bytes = None

# Input ground truth for WER
st.session_state.ground_truth = st.text_input(
    "Enter ground truth for WER calculation (Optional)",
    value=st.session_state.ground_truth,
    key="ground_truth_input"
)

# Load model and processor
@st.cache_resource
def load_model():
    model_repo_name = "wy0909/Whisper-MixedLanguageModel"
    model = WhisperForConditionalGeneration.from_pretrained(model_repo_name)
    processor = WhisperProcessor.from_pretrained(model_repo_name)
    model.config.forced_decoder_ids = None
    model.generation_config.forced_decoder_ids = None
    model.config.suppress_tokens = []
    return model, processor

model, processor = load_model()

def capitalize_sentences(text):
    sentences = re.split(r'(?<=[.!?]) +', text)
    capitalized = [s.strip().capitalize() for s in sentences]
    return ' '.join(capitalized)

# Transcription Mode
mode = st.selectbox(
    "Transcription Mode:",
    options=["With Whisper", "With Whisper and WER"],
    help="Select transcription mode"
)

# Transcription Button
if st.button("📝 Transcribe"):
    if not st.session_state.audio_bytes:
        st.error("Please upload or record an audio file first.")
    else:
        start_time = time.time()
        try:
            waveform, sample_rate = torchaudio.load(st.session_state.audio_path)
            waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
            inputs = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
            with torch.no_grad():
                predicted_ids = model.generate(inputs["input_features"])
            transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
            st.session_state.predicted_text = capitalize_sentences(transcription)
            st.markdown("### 🎤 Transcription Result")
            st.success(st.session_state.predicted_text)

            if mode == "With Whisper and WER" and st.session_state.ground_truth:
                st.session_state.wer_value = wer(st.session_state.ground_truth.lower(), st.session_state.predicted_text.lower())
                st.markdown("### 🧮 Word Error Rate (WER)")
                st.write(f"WER: `{st.session_state.wer_value:.2f}`")

        except Exception as e:
            st.error(f"❌ Transcription failed: {str(e)}")

        end_time = time.time()
        duration = end_time - start_time
        st.markdown(f"<div style='font-size: 10px; color: gray;'>Processed in {duration:.2f}s</div>", unsafe_allow_html=True)