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import gradio as gr
import subprocess
command = "git clone https://github.com/OlaWod/FreeVC.git"
subprocess.run(command, shell=True)
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() |