Generate Audio from Text Scripts using Self-Hosted Bark Model and Google Drive

## Audio Generator - Documentation Purpose: Generate audio files from text scripts stored in Google Drive. ### Flow: 1. Receive repo IDs. 2. Fetch text scripts. 3. Generate .wav files using local Bark model. 4. Upload back to Drive. ### Dependencies: - Python script: `/scripts/generate_voice.py` - Bark (voice generation system) - n8n instance with access to local shell - Google Drive OAuth2 credentials ### Notes: - Script filenames must end with `.txt` - Only works with plain text - No external API used = 100% free ### `/scripts/generate_voice.py`: ```python import sys import torch import numpy import re from bark import SAMPLE_RATE, generate_audio, preload_models from scipy.io.wavfile import write as write_wav # Patch to allow numpy._core.multiarray.scalar during loading torch.serialization.add_safe_globals([numpy._core.multiarray.scalar]) # Monkey patch torch.load to force weights_only=False _original_torch_load = torch.load def patched_torch_load(f, *args, **kwargs): if 'weights_only' not in kwargs: kwargs['weights_only'] = False return _original_torch_load(f, *args, **kwargs) torch.load = patched_torch_load # Preload Bark models preload_models() def split_text(text, max_len=300): # Split on punctuation to avoid mid-sentence cuts sentences = re.split(r'(?<=[.?!])\s+', text) chunks = [] current = "" for sentence in sentences: if len(current) + len(sentence) < max_len: current += sentence + " " else: chunks.append(current.strip()) current = sentence + " " if current: chunks.append(current.strip()) return chunks # Input text file and output path input_text_path = sys.argv[1] output_wav_path = sys.argv[2] with open(input_text_path, 'r', encoding='utf-8') as f: full_text = f.read() voice_preset = "v2/en_speaker_7" chunks = split_text(full_text) # Generate and concatenate audio chunks audio_arrays = [] for chunk in chunks: print(f"Generating audio for chunk: {chunk[:50]}...") audio = generate_audio(chunk, history_prompt=voice_preset) audio_arrays.append(audio) # Merge all audio chunks final_audio = numpy.concatenate(audio_arrays) # Write final .wav file write_wav(output_wav_path, SAMPLE_RATE, final_audio) print(f"Full audio generated at: {output_wav_path}") ```