KandirResearch/CiSiMi-v0.1

CiSiMi: A Text-to-Speech TTS Model

Overview

CiSiMi is an early prototype of a text-to-audio model that can process text inputs and respond with both text and audio. Built for resource-constrained environments, it's designed to run efficiently on CPU using llama.cpp, making advanced speech synthesis accessible even without powerful GPUs.

"Being GPU poor and slightly disappointed with the csm release and my inability to run it, having to wait for time it takes me to run an ASR+LLM+TTS combo, I decided to ask Mom and Mom gave me CiSiMi At Home!"

This project demonstrates the power of open-source tools to create accessible speech technology. While still in its early stages, it represents a step toward democratizing advanced text-to-audio capabilities.

Technical Details

Model Specifications

• Architecture: Based on OuteTTS-0.3-500M
• Languages: English
• Pipeline: Text-to-audio
• Parameters: 500M
• Training Dataset Size: ~15k samples
• Future Goals: Scale to 200k-500k dataset with multi-turn conversation using both a 500M and a 1B parameter model variants, plus adding streaming for realtime.

Training Methodology

1. Dataset Preparation:

   • Started with gruhit-patel/alpaca_speech_instruct
   • Cleaned by removing code, mathematical expressions, and non-English content
   • Filtered to keep only entries with input+output texts of 256 tokens or less

2. Audio Generation:

   • Converted text outputs to speech using hexgrad/Kokoro-82M
   • Verified each audio generation using OpenAI Whisper
   • Published the resulting dataset as KandirResearch/Speech2Speech

3. Model Training:

   • Preprocessed dataset using modified OuteTTS methodology (training details)
   • Fine-tuned OuteAI/OuteTTS-0.3-500M using Unsloth SFT
   • Trained for 6 epochs reaching a loss of 2.27 as a proof of concept
   • Trained for 3 epochs reaching a loss of 2.42 as a proof of concept

Usage Guide

Sample

Explain to me how gravity works!

Installation

pip install outetts llama-cpp-python --upgrade
pip install huggingface_hub sounddevice

Implementation

import torch
import outetts
import numpy as np
from huggingface_hub import hf_hub_download
from outetts.wav_tokenizer.audio_codec import AudioCodec
from outetts.version.v2.prompt_processor import PromptProcessor
from outetts.version.playback import ModelOutput

# Download the model
model_path = hf_hub_download(
    repo_id="KandirResearch/CiSiMi-v0.1",
    filename="unsloth.Q8_0.gguf",
)

# Configure the model
model_config = outetts.GGUFModelConfig_v2(
    model_path=model_path,
    tokenizer_path="KandirResearch/CiSiMi-v0.1",
)

# Initialize components
interface = outetts.InterfaceGGUF(model_version="0.3", cfg=model_config)
audio_codec = AudioCodec()
prompt_processor = PromptProcessor("KandirResearch/CiSiMi-v0.1")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
gguf_model = interface.get_model()

# Helper function to extract audio from tokens
def get_audio(tokens):
    outputs = prompt_processor.extract_audio_from_tokens(tokens)
    if not outputs:
        return None
    audio_tensor = audio_codec.decode(torch.tensor([[outputs]], dtype=torch.int64).to(device))
    return ModelOutput(audio_tensor, audio_codec.sr)

# Helper function to clean text output
def extract_text_from_tts_output(tts_output):
    text = ""
    for line in tts_output.strip().split('\n'):
        if '<|audio_end|>' in line or '<|im_end|>' in line:
            continue
        if '<|' in line:
            word = line.split('<|')[0].strip()
            if word:
                text += word + " "
        else:
            text += line.strip() + " "
    return text.strip()

# Generate response function
def generate_response(instruction):
    prompt = f"<|im_start|>\nInstructions:\n{instruction}\n<|im_end|>\nAnswer:\n"
    gen_cfg = outetts.GenerationConfig(
        text=prompt, 
        temperature=0.6, 
        repetition_penalty=1.1, 
        max_length=4096, 
        speaker=None
    )
    
    input_ids = prompt_processor.tokenizer.encode(prompt)
    tokens = gguf_model.generate(input_ids, gen_cfg)
    
    output_text = prompt_processor.tokenizer.decode(tokens, skip_special_tokens=False)
    
    if "<|audio_end|>" in output_text:
        first_part, _, _ = output_text.partition("<|audio_end|>")
        
        if "<|audio_end|>\n<|im_end|>\n" not in first_part:
            first_part += "<|audio_end|>\n<|im_end|>\n"
            
        extracted_text = extract_text_from_tts_output(first_part)
        
        audio_start_pos = first_part.find("<|audio_start|>\n") + len("<|audio_start|>\n")
        audio_end_pos = first_part.find("<|audio_end|>\n<|im_end|>\n") + len("<|audio_end|>\n<|im_end|>\n")
        
        if audio_start_pos >= len("<|audio_start|>\n") and audio_end_pos > audio_start_pos:
            audio_tokens_text = first_part[audio_start_pos:audio_end_pos]
            audio_tokens = prompt_processor.tokenizer.encode(audio_tokens_text)
            audio_output = get_audio(audio_tokens)
            
            if audio_output is not None and hasattr(audio_output, 'audio') and audio_output.audio is not None:
                audio_numpy = audio_output.audio.cpu().numpy()
                if audio_numpy.ndim > 1:
                    audio_numpy = audio_numpy.squeeze()
                
                return extracted_text, (audio_output.sr, audio_numpy)
    
    return output_text, None

# Example usage
question = "What is the meaning of life?"
response_text, response_audio = generate_response(question)
print(response_text)

# Play audio if available
if response_audio is not None:
    if "ipykernel" in sys.modules:
        from IPython.display import display, Audio
        display(Audio(response_audio[1], rate=response_audio[0], autoplay=True))
    else:
        import sounddevice as sd
        sd.play(response_audio[1], samplerate=response_audio[0])
        sd.wait()

Limitations & Future Work

This early prototype has several areas for improvement:

• Limited training data (~15k samples)
• Basic prompt/chat template structure
• Opportunity to optimize training hyperparameters
• Potential for multi-turn conversation capabilities

Potential Limitation: This type of model quickly fills up context window, making smaller models generally more practical for implementation.

Acknowledgments & Citations

This model builds on the following open-source projects:

1. OuteAI/OuteTTS-0.3-500M - Base model
2. gruhit-patel/alpaca_speech_instruct - Initial dataset
3. hexgrad/Kokoro-82M - TTS generation
4. OpenAI Whisper - Speech verification
5. Unsloth - Training optimization