thenextgentechinsider/lora_model

Here's how you can run it on Windows 11 to convert text to speech:

Prerequisites:

1. Python: Ensure you have Python installed. You can download it from python.org. Version 3.8+ is recommended. During installation, make sure to check "Add Python to PATH".
2. PyTorch: This is the core deep learning library.
   • Go to pytorch.org.
   • Select the appropriate options for your system (PyTorch Build: Stable, Your OS: Windows, Package: Pip, Language: Python, Compute Platform: CUDA if you have an NVIDIA GPU and want to use it, otherwise CPU).
   • Copy the generated pip install command and run it in your command prompt or terminal.
   • Example for CPU: pip3 install torch torchvision torchaudio
   • Example for CUDA 11.8: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 (Check the PyTorch website for the latest CUDA version command if you have a compatible NVIDIA GPU).
3. Transformers Library: From Hugging Face.

   pip install transformers
   

4. SentencePiece: Often required by tokenizers.

   pip install sentencepiece
   

5. SoundFile: To save the audio as a .wav file.

   pip install soundfile
   ```6.  **Accelerate (Recommended):** For efficient model loading and execution.
   ```bash
   pip install accelerate
   

6. Unzip your model: Unzip merged_16bit_model.zip. You should have a folder (let's assume it's named my_sesame_tts_model after you rename the unzipped "model" folder, or you can use "model" directly) containing files like pytorch_model.bin (or .safetensors), config.json, preprocessor_config.json, etc.

Steps to Run Text-to-Speech:

1. Create a Project Folder: Create a new folder for your project, for example, C:\my_tts_project.

2. Place Your Model: Move or copy the unzipped model folder (e.g., my_sesame_tts_model) into your project folder (C:\my_tts_project\my_sesame_tts_model).

3. Create a Python Script: Inside your project folder (C:\my_tts_project), create a new Python file, for example, run_tts.py. Paste the following code into it:

   import torch
   from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
   import soundfile as sf
   import os
   
   # --- Configuration ---
   MODEL_PATH = "./my_sesame_tts_model"  # Path to your downloaded and unzipped model folder
   OUTPUT_FILENAME = "output_audio.wav"
   DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
   # Set a default sampling rate if not found in config, check your model's training details
   DEFAULT_SAMPLING_RATE = 24000
   
   def text_to_speech(text, speaker_id, model_path, output_filename):
       print(f"Using device: {DEVICE}")
   
       # 1. Load processor and model
       try:
           print(f"Loading processor from: {model_path}")
           processor = AutoProcessor.from_pretrained(model_path)
           print(f"Loading model from: {model_path}")
           # For merged 16-bit models, ensure your PyTorch can handle bfloat16 or float16
           # If you saved as full float32, it will also work.
           model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path).to(DEVICE)
           model.eval() # Set model to evaluation mode
       except Exception as e:
           print(f"Error loading model or processor: {e}")
           print("Make sure the MODEL_PATH is correct and all necessary files are present.")
           return
   
       # 2. Prepare input text
       # The format "[speaker_id]text" was used in the Colab notebook.
       # Adjust if your model expects a different format.
       formatted_text = f"[{speaker_id}]{text}"
       print(f"Input text: \"{formatted_text}\"")
   
       try:
           inputs = processor(text=formatted_text, return_tensors="pt").to(DEVICE)
       except Exception as e:
           print(f"Error during text processing: {e}")
           return
   
       # 3. Generate audio
       print("Generating audio...")
       try:
           with torch.no_grad(): # Disable gradient calculations for inference
                # The generate function might have specific arguments for your model
                # The `output_audio=True` argument was in the Colab notebook,
                # but standard AutoModelForSpeechSeq2Seq might just return audio in `output.audio`
                # or directly as the output if it's the primary modality.
                # Let's try a common approach first.
               generated_ids = model.generate(**inputs)
   
               # The output structure can vary.
               # For Bark-like models, it might be in generated_ids["audio"] or similar.
               # For Sesame models, it's often directly in the output or a specific key.
               # The notebook used: audio_values = model.generate(**inputs, output_audio=True)
               # Let's try to replicate that if `output_audio` is a valid generate param for this model.
               # Otherwise, inspect `generated_ids`.
               # For many speech models, the output is directly the audio waveforms.
               if hasattr(generated_ids, 'audio'): # Check for an 'audio' attribute
                   audio_values = generated_ids.audio[0]
               elif isinstance(generated_ids, torch.Tensor) and generated_ids.ndim >= 2 : # If output is a tensor of waveforms
                    audio_values = generated_ids[0]
               else:
                   # If unsure, try to force output_audio if the model supports it (experimental)
                   try:
                       output = model.generate(**inputs, output_audio=True) # Specific to some models
                       audio_values = output[0] # Assuming it returns a tuple/list
                   except TypeError:
                       print("Model does not support `output_audio=True` directly or output structure is unexpected.")
                       print(f"Inspect `generated_ids` structure: {type(generated_ids)}")
                       if isinstance(generated_ids, dict): print(f"Keys: {generated_ids.keys()}")
                       return
       except Exception as e:
           print(f"Error during audio generation: {e}")
           return
   
       # 4. Post-process and save
       audio_np = audio_values.cpu().to(torch.float32).numpy()
   
       # Determine sampling rate
       sampling_rate = DEFAULT_SAMPLING_RATE
       if hasattr(model.config, 'sampling_rate'):
           sampling_rate = model.config.sampling_rate
       elif hasattr(processor, 'feature_extractor') and hasattr(processor.feature_extractor, 'sampling_rate'):
           sampling_rate = processor.feature_extractor.sampling_rate
       else:
           print(f"Could not automatically determine sampling rate. Using default: {DEFAULT_SAMPLING_RATE} Hz.")
       print(f"Using sampling rate: {sampling_rate} Hz")
   
   
       try:
           sf.write(output_filename, audio_np, samplerate=sampling_rate)
           print(f"Audio saved to {os.path.abspath(output_filename)}")
       except Exception as e:
           print(f"Error saving audio file: {e}")
   
   if __name__ == "__main__":
       input_text = "Hello, this is a test of my fine-tuned text to speech model."
       speaker_id_to_use = 1  # Change this to a valid speaker_id for your model
   
       # Ensure the model path is correct relative to the script location
       # or provide an absolute path.
       actual_model_path = os.path.join(os.path.dirname(__file__), MODEL_PATH)
   
       text_to_speech(input_text, speaker_id_to_use, actual_model_path, OUTPUT_FILENAME)
   
       # Example with a different speaker
       # input_text_2 = "Unsloth makes training models faster."
       # speaker_id_2 = 2
       # text_to_speech(input_text_2, speaker_id_2, actual_model_path, "output_audio_speaker2.wav")
   

4. Customize the Script:

   • MODEL_PATH: Double-check this path. If run_tts.py is in C:\my_tts_project and your model is in C:\my_tts_project\my_sesame_tts_model, then ./my_sesame_tts_model is correct.
   • input_text: Change this to the text you want to convert.
   • speaker_id_to_use: Crucially, change this to a valid speaker ID that your model was trained on or supports. The Colab notebook used 1 and 2 as examples.

5. Run the Script:

   • Open a Command Prompt or PowerShell.
   • Navigate to your project folder: cd C:\my_tts_project
   • Run the script: python run_tts.py

   This will generate an audio file (e.g., output_audio.wav) in your project folder.

Important Considerations for Sesame TTS Models:

• Processor and Input Format: The way you format the input text (f"[{speaker_id}]{text}") is critical and depends on how the processor for your specific Sesame model variant was trained to expect it. The format used in the script is based on common patterns seen in the Unsloth examples. If it doesn't work, you might need to inspect the processor.apply_chat_template method or how inputs were prepared in the original notebook more closely.
• Sampling Rate: The script tries to get the sampling rate from model.config.sampling_rate or processor.feature_extractor.sampling_rate. The Colab notebook mentioned 24000 Hz, so I've set it as a default. Ensure this matches your model.
• model.generate() output: The structure of the output from model.generate() can vary. The provided script tries a common way and then a fallback. If audio generation fails or the output is not as expected, you might need to print type(generated_ids) and generated_ids.keys() (if it's a dict) to understand its structure and extract the audio data correctly. The Colab notebook used audio_values = model.generate(**inputs, output_audio=True). The script attempts to replicate this logic if standard methods don't apply.
• GPU Memory: 1B parameter models, even at 16-bit, can require a decent amount of VRAM if run on GPU. If you run into CUDA out-of-memory errors, try with DEVICE = "cpu" (though it will be much slower).
• Unsloth specific loading: While save_pretrained_merged aims for standard Hugging Face format, if AutoModelForSpeechSeq2Seq.from_pretrained fails in an Unsloth-specific way, you might theoretically need to see if Unsloth provides a specific loading function for merged models for inference. However, this is usually not the case; AutoModel should work.

This comprehensive guide should get you started. The key is ensuring the paths are correct, all libraries are installed, and the input format matches what your specific fine-tuned Sesame model expects.