---
base_model: unsloth/csm-1b
library_name: peft
license: mit
datasets:
- Dev372/Cardiology_Medical_STT_Dataset
language:
- en
pipeline_tag: text-to-speech
tags:
- cardiology
- medical
- transformers
---

# Model Card for Cardiology-TTS

<!-- Provide a quick summary of what the model is/does. -->


## Model Details

This is a fine-tuned version of the Conversational Speech Model (CSM-1B) using LoRA for parameter-efficient fine-tuning. 
The model is trained on a 1,530-sample dataset of medical cardiology texts, designed to generate high-quality speech from cardiology-related text.
It leverages the capabilities of the original CSM-1B model for text-to-speech synthesis, extended with domain-specific terminology for medical cardiology.
It is intended for speech generation in English, especially for clinical and educational contexts.

## Uses

### Direct Use

- Text-to-Speech (TTS) generation for cardiology educational content, medical reports, or clinical explanations.
- Integrating spoken content in healthcare apps, e-learning platforms, or patient-facing tools for cardiology topics.
- Research and prototyping domain-specific TTS applications using small medical datasets.


## Bias, Risks, and Limitations

- Small training dataset (2K samples) → Model may not generalize well to rare medical terms, long passages, or other medical domains outside cardiology.
- English-only support → Model is not trained for other languages.
- TTS artifacts → Some generated audio may contain unnatural pauses, mispronunciations, or clipping in challenging sentences.
- Not for diagnostic purposes → Model outputs speech for educational/illustrative purposes and should not be used for medical diagnosis or patient instructions.
- Model size and resources → CSM-1B is large; requires GPU for real-time inference and significant VRAM for batch synthesis.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import torch
from transformers import CsmForConditionalGeneration, AutoProcessor
import soundfile as sf
from peft import PeftModel


model_id = "unsloth/csm-1b"
device = "cuda" if torch.cuda.is_available() else "cpu"


processor = AutoProcessor.from_pretrained(model_id)
base_model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

model = PeftModel.from_pretrained(base_model, "khazarai/Cardiology-TTS")

text = "The coronary arteries are patent with no significant stenosis."

speaker_id = 0

conversation = [
    {"role": str(speaker_id), "content": [{"type": "text", "text": text}]},
]
audio_values = model.generate(
    **processor.apply_chat_template(
        conversation,
        tokenize=True,
        return_dict=True,
    ).to("cuda"),
    max_new_tokens=200, 
    # play with these parameters to tweak results
    # depth_decoder_top_k=0,
    # depth_decoder_top_p=0.9,
    # depth_decoder_do_sample=True,
    # depth_decoder_temperature=0.9,
    # top_k=0,
    # top_p=1.0,
    # temperature=0.9,
    # do_sample=True,
    #########################################################
    output_audio=True
)
audio = audio_values[0].to(torch.float32).cpu().numpy()
sf.write("example.wav", audio, 24000)

```

## Training Details

### Training Data

- Dataset: Dev372/Cardiology_Medical_STT_Dataset

1,530 samples of cardiology-related text paired with audio.


### Framework versions

- PEFT 0.15.2