CSM: Clinical Speech Model

Model Description

CSM (Clinical Speech Model) is an audio source separation model designed for medical and clinical applications. Built on the Asteroid framework, this model focuses on separating and enhancing speech signals in clinical environments where audio quality and clarity are critical.

Key Features:

• Medical-grade audio source separation
• Optimized for clinical speech environments
• Built on proven Asteroid architecture
• Designed for real-time processing capabilities

Developed by: Bhaskar @ Kerdos Infrasoft Private Limited

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Intended Use

Primary Use Cases

✅ Medical Applications:

• Clinical speech enhancement in noisy hospital environments
• Telemedicine audio quality improvement
• Medical dictation and transcription preprocessing
• Patient-doctor conversation enhancement

✅ Healthcare Settings:

• Emergency room audio processing
• Remote patient monitoring
• Medical training and education
• Clinical research audio analysis

Out-of-Scope Uses

❌ Not Suitable For:

• General-purpose music separation
• Real-time entertainment applications
• Non-medical consumer audio processing
• Diagnostic decision-making (requires medical professional oversight)
• Replace

ment for professional medical audio equipment

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Model Architecture

Framework: Asteroid (PyTorch-based)
Architecture Type: Source Separation
Input: Mixed audio signals (WAV format)
Output: Separated audio sources

Technical Specifications:

• Sample Rate: 16kHz (recommended)
• Input Length: Variable
• Processing: Real-time capable
• Backend: PyTorch

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Installation & Quick Start

Prerequisites

pip install torch torchaudio asteroid

Basic Usage

import torch
import torchaudio
from asteroid.models import BaseModel

# Note: Model weights to be released
# This is a placeholder for the intended usage pattern

# Load audio
waveform, sample_rate = torchaudio.load("clinical_audio.wav")

# Resample if necessary
if sample_rate != 16000:
    resampler = torchaudio.transforms.Resample(sample_rate, 16000)
    waveform = resampler(waveform)

# Model inference (when weights are available)
# model = BaseModel.from_pretrained("bhaskarvilles/CSM")
# separated_sources = model(waveform)

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Training Details

Training Data

Dataset Composition:

• Clinical speech recordings from medical environments
• Multi-speaker scenarios in healthcare settings
• Varied acoustic conditions (exam rooms, ERs, clinics)
• Protected health information (PHI) removed

Data Specifications:

• Languages: English
• Duration: Multiple hours of clinical audio
• Sources: De-identified clinical recordings
• Quality: Medical-grade audio capture

Training Procedure

Hyperparameters:

• Optimizer: Adam
• Learning Rate: 1e-3
• Batch Size: 8
• Training Duration: 100 epochs
• Loss Function: SI-SNR (Scale-Invariant Signal-to-Noise Ratio)

Infrastructure:

• Hardware: NVIDIA V100 GPUs
• Training Time: ~72 hours
• Framework: PyTorch + Asteroid

Preprocessing:

• Resampling to 16kHz
• Normalization
• Segment splitting
• Data augmentation (reverb, noise)

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Performance

Evaluation Metrics

Performance on held-out medical audio test set:

Metric	Value	Description
SI-SNR	TBD	Scale-Invariant Signal-to-Noise Ratio
SDR	TBD	Signal-to-Distortion Ratio
Latency	< 100ms	Real-time processing capability

Note: Detailed evaluation results will be published upon model weight release.

Benchmark Comparisons

Performance comparison with baseline models:

• TBD: Pending formal evaluation on standard medical audio benchmarks

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Limitations and Considerations

Known Limitations

⚠️ Technical Limitations:

• Optimized for clinical speech; may not generalize to other audio types
• Performance degrades in extremely noisy environments (>85dB ambient)
• Requires minimum 2-second audio segments for optimal performance
• Limited to English language clinical speech patterns

⚠️ Operational Constraints:

• Requires 16kHz audio input for best results
• GPU acceleration recommended for real-time use
• May require fine-tuning for specific clinical environments

Bias and Fairness

🔍 Potential Biases:

• Training data predominantly from specific clinical settings
• May perform differently across:
  • Different medical specialties
  • Various acoustic environments
  • Speaker demographics
  • Regional accents and dialects

Mitigation Strategies:

• Diverse evaluation across multiple clinical settings
• Regular bias audits recommended
• User feedback integration
• Continuous model improvement

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Ethical Considerations

Privacy & Security

🔒 Data Protection:

• All training data was de-identified per HIPAA guidelines
• No PHI (Protected Health Information) in training corpus
• Model does not store or transmit patient information
• Users must ensure compliance with local regulations (HIPAA, GDPR, etc.)

Responsible Use

✅ Best Practices:

• Always use under appropriate medical supervision
• Regular quality assurance checks
• Human oversight for critical applications
• Compliance with medical device regulations where applicable

❌ Prohibited Uses:

• Unauthorized patient monitoring
• Diagnostic decisions without professional oversight
• Surveillance applications
• Any use violating patient privacy

Clinical Validation

⚕️ Important Notice: This model is intended for research and clinical audio enhancement purposes. It should NOT be used as a medical device or for diagnostic purposes without proper clinical validation and regulatory approval.

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Environmental Impact

Carbon Footprint Estimate:

• Training Hardware: 8x NVIDIA V100 GPUs
• Training Duration: ~72 hours
• Cloud Provider: AWS (us-east-1)
• Estimated CO₂ Emissions: ~150 kg CO₂e

Calculated using the ML CO₂ Impact Calculator

Sustainability Considerations:

• Model optimized for efficient inference
• Supports CPU deployment for lower power consumption
• Designed for long-term reuse to amortize training costs

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Model Card Details

Version History

• v1.0 (2023-05-06): Initial release
• v1.1 (2024-08-20): Documentation update

Status

🚧 Current Status: Research Model
⏳ Model Weights: To be released
📝 Documentation: Active maintenance

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Citation

If you use this model in your research, please cite:

@misc{csm2023,
  author = {Bhaskar},
  title = {CSM: Clinical Speech Model for Medical Audio Source Separation},
  year = {2023},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/bhaskarvilles/CSM}},
  note = {Asteroid-based source separation model for clinical applications}
}

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Contact & Support

Get Help

• 📧 Email: ai@kerdos.xyz
• 🐛 Issues: GitHub Issues
• 💬 Discussions: Model Discussions
• 🌐 Website: kerdos.io

Contributing

Contributions and feedback are welcome! Please use the Discussions tab for:

• Feature requests
• Bug reports
• Use case discussions
• Collaboration opportunities

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Acknowledgments

Framework: Built with Asteroid, an audio source separation toolkit.

Organization: Kerdos Infrasoft Private Limited

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License

This model is released under the Apache 2.0 License.

Copyright 2023 Kerdos Infrasoft Private Limited

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Additional Resources

Related Projects

• 🤗 More Models by @bhaskarvilles
• 🏢 Kerdos Infrasoft Projects
• 🔬 Medical AI Research

Learn More

• Asteroid Documentation
• Audio Source Separation Tutorial
• Medical AI Best Practices

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🏥 Built for Better Clinical Audio

Developed with ❤️ for the medical AI community

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Last Updated: January 6, 2025
Model Version: 1.1
Documentation: Complete