Update README.md

README.md

@@ -139,4 +139,91 @@ See `example_tts.py` for more examples.
 Every audio file generated by Chatterbox includes [Resemble AI's Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth) - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy.
 
 # Disclaimer
-Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.
+Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.
+
+
+# Use EN
+```sh
+uv init --python 3.11
+uv sync
+source .venv/bin/activate
+git clone https://github.com/resemble-ai/chatterbox.git
+cd chatterbox
+# remove gradio and russian tts... from pyproject.toml
+python -m pip install -e .
+
+# mac run:
+python example_for_mac.py
+```
+
+# use FA
+```py
+#example_fa.py
+from chatterbox.mtl_tts import ChatterboxMultilingualTTS
+import torch
+import torchaudio as ta
+from safetensors.torch import load_file as load_safetensors
+from huggingface_hub import hf_hub_download, login
+import os
+
+# Detect device (Mac with M1/M2/M3/M4)
+device = "mps" if torch.backends.mps.is_available() else "cpu"
+map_location = torch.device(device)
+
+torch_load_original = torch.load
+def patched_torch_load(*args, **kwargs):
+    if 'map_location' not in kwargs:
+        kwargs['map_location'] = map_location
+    return torch_load_original(*args, **kwargs)
+
+torch.load = patched_torch_load
+
+# Load the multilingual TTS model, making sure it uses the CPU
+multilingual_model = ChatterboxMultilingualTTS.from_pretrained(device)
+
+# read token
+token = "YOUR_TOKEN"
+login(token)
+
+# Define the model repo and file path
+model_repo = "Thomcles/Chatterbox-TTS-Persian-Farsi"
+file_name = "t3_fa.safetensors"
+
+# Define the cache directory (your custom local folder)
+cache_dir = "./cacheModel"
+
+# Create the cache directory if it doesn't exist
+os.makedirs(cache_dir, exist_ok=True)
+
+# Download the model weights to the specified cache directory
+file_path = hf_hub_download(repo_id=model_repo, filename=file_name, cache_dir=cache_dir)
+
+print(f"Model weights downloaded to: {file_path}")
+
+# Load the T3 model state dict for Persian, explicitly mapping to CPU
+# Use `torch.load` with map_location to ensure it loads on the CPU
+t3_state = load_safetensors(file_path, device='cpu')
+
+# Load the T3 model's state dict into the multilingual model and move it to the CPU
+multilingual_model.t3.load_state_dict(t3_state)
+multilingual_model.t3.to(device).eval() # Ensure it's on CPU
+
+# Define the Persian text you want to convert to speech
+persian_text = "سلام! به آزمایش تبدیل متن به گفتار خوش آمدید."
+
+
+# Generate the speech for the provided Persian text
+AUDIO_PROMPT_PATH = "target_voice.wav"
+wav_persian = multilingual_model.generate(
+    persian_text,
+    language_id=None,
+    audio_prompt_path=AUDIO_PROMPT_PATH,
+    exaggeration=0.5,
+    cfg_weight=0.5
+)
+
+# Save the generated speech to a WAV file
+ta.save("test-fa.wav", wav_persian, multilingual_model.sr)
+
+print("Speech synthesis complete, saved as 'test-fa.wav'")
+```