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 :

 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}")