---
language: en
license: gpl-3.0
tags:
- speech-to-text
- text-correction
- qwen3
- speech-processing
- transcription-cleaning
datasets:
- custom
base_model: Qwen/Qwen3-1.7B
---

# Qwen3-1.7B Sussurro - v1.0

A fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) for speech-to-text transcription correction.

## Model Description

This model converts raw speech transcriptions into clean, written-quality text by:

- **Removing filler words**: um, uh, like, you know, I mean, actually, literally, right, you see
- **Fixing stuttering**: the the → the, we we → we, I I → I
- **Eliminating false starts**: "I was- actually, I mean..." → clean phrasing
- **Converting conversational to written**: Transform spoken language patterns to formal written text
- **Organizing rambling speech**: Convert stream-of-consciousness to structured sentences
- **Preserving meaning**: Maintain all important content and intent

## Training Details

- **Base Model**: Qwen/Qwen3-1.7B
- **Training Method**: QLoRA (4-bit quantization + LoRA adapters)
- **Training Data**: 3,997 speech transcription pairs
- **Hardware**: AMD Radeon RX 7800 XT (16GB VRAM) with ROCm
- **Training Duration**: ~4 hours

### Training Configuration

- **Quantization**: 4-bit NF4 with double quantization
- **LoRA**: rank=64, alpha=128, targeting all attention and MLP layers
- **Batch Size**: 2 per device, 32 gradient accumulation (effective batch=64)
- **Learning Rate**: 2e-4 with cosine schedule
- **Epochs**: 3
- **Optimizer**: paged_adamw_8bit

## Evaluation Results

- **BLEU-4**: 0.461
- **ROUGE-1**: 0.785
- **ROUGE-2**: 0.652
- **ROUGE-L**: 0.748
- **Test Samples**: 401

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "cesp99/qwen3-sussurro"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# System prompt
system_prompt = """You are a speech-to-text correction specialist. Your task is to convert raw speech transcriptions into clean, written text by:
- Removing all filler words (um, uh, like, you know, I mean, actually, literally, right, you see)
- Fixing stuttering and repeated words (the the → the, we we → we)
- Eliminating false starts and self-corrections
- Converting conversational speech patterns to formal written language
- Organizing rambling thoughts into clear, structured sentences
- Preserving all important meaning and content"""

# Example correction
raw_speech = "so, uh, I was thinking like maybe we could, you know, meet up on Saturday?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": raw_speech},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
    do_sample=True,
)

corrected_text = tokenizer.decode(
    outputs[0][inputs['input_ids'].shape[1]:],
    skip_special_tokens=True
)

print(corrected_text)
# Output: "I was thinking maybe we could meet up on Saturday?"
```

## Example Corrections

### Example 1: Filler Words
**Input**: "so, uh, I was thinking like maybe we could, you know, meet up on Saturday?"
**Output**: "I was thinking maybe we could meet up on Saturday?"

### Example 2: Stuttering
**Input**: "the the budget report is, uh, almost ready and we we just need to finalize"
**Output**: "The budget report is almost ready and we just need to finalize it."

### Example 3: False Starts
**Input**: "I mean, actually, uh, we should probably, like, you know, consider all the options"
**Output**: "We should probably consider all the options before making a decision."

## Use Cases

- **Meeting Transcripts**: Clean up recorded meeting transcriptions
- **Podcast/Interview Processing**: Convert conversational speech to publishable text
- **Voice Notes**: Transform voice memos into written format
- **Content Creation**: Prepare speech-to-text data for articles or documentation
- **Data Cleaning**: Pre-process speech datasets for downstream NLP tasks

## Limitations

- Trained primarily on English speech patterns
- May occasionally over-correct or change intended meaning
- Best suited for conversational speech patterns (not formal presentations)
- Requires careful review for critical applications

## Technical Requirements

- **GPU**: Recommended 8GB+ VRAM for inference
- **Framework**: PyTorch with Transformers library
- **Precision**: BF16 recommended (FP16 also supported)

## License

GNU General Public License v3.0 (GPL-3.0)

This fine-tuned model is licensed under GPL-3.0. Note that the base model (Qwen3-1.7B) is Apache 2.0 licensed.

## Citation

If you use this model, please cite:

```bibtex
@misc{qwen3-sussurro,
  title={Qwen3-1.7B Sussurro},
  author={Carlo Esposito},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/cesp99/qwen3-sussurro}
}
```

## Acknowledgments

- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
- Training framework: Hugging Face Transformers + PEFT
- Quantization: BitsAndBytes

## Training Repository

Full training pipeline and code: [github.com/cesp99/qwen3-sussurro](https://github.com/cesp99/qwen3-sussurro)