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README.md

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----
-library_name: transformers
-tags:
-- unsloth
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
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-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
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-[More Information Needed]
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-#### Summary
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-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+Here's how you can run it on your Windows 11 to convert text to speech:
+
+**Prerequisites:**
+
+1.  **Python:** Ensure you have Python installed. You can download it from [python.org](https://www.python.org/downloads/). 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](https://pytorch.org/get-started/locally/).
+    *   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.
+    ```bash
+    pip install transformers
+    ```
+4.  **SentencePiece:** Often required by tokenizers.
+    ```bash
+    pip install sentencepiece
+    ```
+5.  **SoundFile:** To save the audio as a `.wav` file.
+    ```bash
+    pip install soundfile
+    ```6.  **Accelerate (Recommended):** For efficient model loading and execution.
+    ```bash
+    pip install accelerate
+    ```
+7.  **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:
+
+    ```python
+    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.