Add bark and vc code

app.py

@@ -1,7 +1,132 @@
 import gradio as gr
+import subprocess
 
-def greet(name):
-    return "Hello " + name + "!!"
+command = "git clone https://github.com/OlaWod/FreeVC.git"
+subprocess.run(command, shell=True)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+command = "git clone https://github.com/OlaWod/FreeVC.git"
+subprocess.run(command, shell=True)
+
+import gradio as gr
+import numpy as np
+import os
+from scipy.io.wavfile import write
+import tempfile
+import zipfile
+import shutil
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+from IPython.display import Audio
+import nltk  # we'll use this to split into sentences
+import subprocess
+
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from IPython.display import Audio, display
+import numpy as np
+
+from bark.generation import (
+    generate_text_semantic,
+    preload_models,
+)
+from bark.api import semantic_to_waveform
+from bark import generate_audio, SAMPLE_RATE
+
+# Preload models if necessary
+preload_models()
+
+def process_audio_files_with_logging(script, speaker, cloneFile):
+    log_messages = "Starting audio processing...\n"
+    sentences = script.split('\n')
+    sentences = [item.strip() for item in sentences if item.strip()]
+    GEN_TEMP = 0.4  # Example temperature, adjust as necessary
+
+    temp_dir = tempfile.mkdtemp()
+
+    for idx, sentence in enumerate(sentences):
+        log_messages += f"Processing sentence {idx + 1}: {sentence}\n"
+        semantic_tokens = generate_text_semantic(
+            sentence,
+            history_prompt=speaker,
+            temp=GEN_TEMP,
+            min_eos_p=0.05,
+        )
+
+        audio_array = semantic_to_waveform(semantic_tokens, history_prompt=speaker)
+        filename = os.path.join(temp_dir, f"audio_{idx:02d}.wav")
+        write(filename, SAMPLE_RATE, audio_array)
+        log_messages += f"Generated audio for sentence {idx + 1}.\n"
+
+    log_messages += "All sentences processed. Starting silence reduction...\n"
+
+    # Process each file to remove or reduce silence
+    for root, _, files in os.walk(temp_dir):
+        with open("FreeVC/convert.txt", "w") as f:
+            for file in files:
+                file_path = os.path.join(root, file)
+                audio = AudioSegment.from_file(file_path, format="wav")
+
+                # Detect non-silent chunks and process
+                processed_audio = process_audio_for_silence(audio, log_messages)
+
+                # Overwrite the original file with processed audio
+                processed_audio.export(file_path, format="wav")
+
+                file_name_without_extension, file_extension = os.path.splitext(file)
+                line = f"{file_name_without_extension}|{file_path}|{cloneFile[0]}\n"
+                f.write(line)
+                log_messages += line + "\n"
+    #command = "python FreeVC/convert.py --hpfile FreeVC/configs/freevc.json --ptfile FreeVC/checkpoints/freevc.pth --txtpath FreeVC/convert.txt --outdir FreeVC/outputs/freevc"
+    #subprocess.run(command, shell=True)
+    log_messages += "Silence reduction complete. Zipping files...\n"
+
+    # Zip the processed files
+    zip_filename = zip_processed_files(temp_dir, log_messages)
+
+    # Clean up the temporary directory
+    shutil.rmtree(temp_dir)
+
+    log_messages += "Processing complete. Files ready for download.\n"
+    return zip_filename, log_messages
+
+def process_audio_for_silence(audio, log_messages):
+    # Parameters for silence detection
+    silence_thresh = -32  # Silence threshold in dB
+    min_silence_len = 1000  # Minimum length of silence to consider in ms
+    keep_silence = 300  # Amount of silence to keep after the silence in ms
+
+    # Detect non-silent chunks
+    non_silent_chunks = split_on_silence(
+        audio,
+        min_silence_len=min_silence_len,
+        silence_thresh=silence_thresh,
+        keep_silence=keep_silence
+    )
+
+    # Combine the non-silent chunks back into a single audio segment
+    processed_audio = AudioSegment.empty()
+    for chunk in non_silent_chunks:
+        processed_audio += chunk
+
+    log_messages += "Audio processed for silence.\n"
+    return processed_audio
+
+def zip_processed_files(temp_dir, log_messages):
+    zip_filename = os.path.join(tempfile.gettempdir(), "processed_audio_files.zip")
+    with zipfile.ZipFile(zip_filename, 'w') as zipf:
+        for root, _, files in os.walk(temp_dir):
+            for file in files:
+                zipf.write(os.path.join(root, file), file)
+
+    log_messages += "Files zipped successfully.\n"
+    return zip_filename
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=process_audio_files_with_logging,
+    inputs=[gr.Textbox(label="Script", lines=10), gr.Dropdown(label="Speaker", choices=[("French","v2/fr_speaker_7"), ("English","v2/en_speaker_7"), ("Japanese","v2/ja_speaker_2"), ("German","v2/de_speaker_6"), ("Hindi","v2/hi_speaker_2"), ("Italian","v2/it_speaker_6"), ("Korean","v2/ko_speaker_0"), ("Polish","v2/pl_speaker_2"), ("Portuguese","v2/pt_speaker_5"), ("Russian","v2/ru_speaker_4"), ("Spanish","v2/es_speaker_0"), ("Turkish","v2/tr_speaker_1")]), gr.Files(label="clone voice")],
+    outputs=[gr.File(label="Download Processed Files"), gr.Textbox(label="Log Messages", lines=20)],
+    title="Audio Processing and Generation",
+    description="Enter a script and select a speaker to generate and process audio files. Process logs will be displayed below."
+)
+
+interface.launch()

requirements.txt

@@ -7,4 +7,6 @@ tensorboard
 torch
 torchvision
 webrtcvad
+pydub
+nltk
 git+https://github.com/suno-ai/bark.git