Create app.py

app.py

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+import os
+import torch
+import numpy as np
+import librosa
+import soundfile as sf
+import streamlit as st
+from tqdm import tqdm
+from speechbrain.pretrained import Tacotron2, HIFIGAN
+
+# Paths
+output_path = "./processed_data/"
+os.makedirs(output_path, exist_ok=True)
+
+# Preprocessing Function
+def preprocess_audio(audio_path, max_length=1000):
+    """
+    Preprocess the audio file to generate mel spectrogram with uniform length.
+    """
+    wav, sr = librosa.load(audio_path, sr=24000)
+    mel_spectrogram = librosa.feature.melspectrogram(
+        y=wav, sr=sr, n_fft=2048, hop_length=256, n_mels=120
+    )
+    mel_spectrogram = np.log(np.maximum(1e-5, mel_spectrogram))  # Log normalization
+
+    # Ensure all mel spectrograms have the same time dimension
+    if mel_spectrogram.shape[1] > max_length:  # Truncate
+        mel_spectrogram = mel_spectrogram[:, :max_length]
+    else:  # Pad
+        padding = max_length - mel_spectrogram.shape[1]
+        mel_spectrogram = np.pad(mel_spectrogram, ((0, 0), (0, padding)), mode="constant")
+
+    return mel_spectrogram
+
+# Function to Split Long Text into Chunks
+def split_text_into_chunks(text, max_chunk_length=200):
+    """
+    Splits the input text into smaller chunks, each of up to `max_chunk_length` characters.
+    """
+    words = text.split()
+    chunks = []
+    current_chunk = []
+    current_length = 0
+
+    for word in words:
+        if current_length + len(word) + 1 > max_chunk_length:
+            chunks.append(" ".join(current_chunk))
+            current_chunk = []
+            current_length = 0
+        current_chunk.append(word)
+        current_length += len(word) + 1  # Account for space
+
+    if current_chunk:
+        chunks.append(" ".join(current_chunk))
+
+    return chunks
+
+# Generate Speech for Long Text
+def generate_speech(text, tacotron2, hifi_gan, output_file="long_speech.wav", sample_rate=24000):
+    """
+    Generates a long speech by splitting the text into chunks, generating audio for each,
+    and concatenating the waveforms.
+    """
+    chunks = split_text_into_chunks(text)
+    waveforms = []
+
+    for chunk in tqdm(chunks, desc="Generating speech"):
+        text_input = [str(chunk)]
+        mel_output, mel_length, alignment = tacotron2.encode_batch(text_input)
+        waveform = hifi_gan.decode_batch(mel_output)
+        waveforms.append(waveform.squeeze().cpu().numpy())
+
+    # Concatenate waveforms
+    long_waveform = np.concatenate(waveforms, axis=0)
+
+    # Save the concatenated audio
+    sf.write(output_file, long_waveform, sample_rate)
+    print(f"Audio has been synthesized and saved as '{output_file}'.")
+
+# Load Pretrained Tacotron2 and HiFi-GAN
+tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tacotron2")
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_hifigan")
+
+# Fine-tuned model (if available)
+if os.path.exists("indic_accent_tacotron2.pth"):
+    tacotron2.load_state_dict(torch.load("indic_accent_tacotron2.pth"))
+    print("Fine-tuned Tacotron2 model loaded successfully.")
+
+# Streamlit UI
+st.title("Text to Speech Generator")
+
+# Text input for the user
+text_input = st.text_area("Enter the text you want to convert to speech:", 
+                          "Good morning, lovely listeners! This is your favorite RJ, Sapna...")
+
+# Button to generate speech
+if st.button("Generate Speech"):
+    if text_input:
+        output_file = "output_long_speech.wav"
+        
+        # Generate speech for the provided text
+        with st.spinner("Generating speech..."):
+            generate_speech(text_input, tacotron2, hifi_gan, output_file)
+
+        # Provide download link
+        st.success("Speech generation complete!")
+        st.audio(output_file, format="audio/wav")
+        st.download_button(label="Download Speech", data=open(output_file, "rb").read(), file_name=output_file, mime="audio/wav")
+    else:
+        st.warning("Please enter some text to generate speech.")