What are Open Source Models for Healthcare Transcription?
Open source models for healthcare transcription are specialized AI systems designed to convert medical speech into accurate text transcripts. Using advanced text-to-speech and speech recognition architectures, they process medical terminology, patient records, and clinical documentation with high precision. This technology enables healthcare providers to automate documentation, reduce transcription costs, and improve patient care efficiency. They foster innovation in medical technology, ensure data privacy through local deployment, and democratize access to powerful healthcare documentation tools, enabling applications from electronic health records to real-time clinical note-taking.
fishaudio/fish-speech-1.5
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model employing an innovative DualAR architecture with dual autoregressive transformer design. It supports multiple languages with over 300,000 hours of training data for English and Chinese, and over 100,000 hours for Japanese. With an ELO score of 1339 in TTS Arena evaluations, it achieves exceptional accuracy with a word error rate (WER) of 3.5% and character error rate (CER) of 1.2% for English, making it ideal for precise healthcare transcription needs.
fishaudio/fish-speech-1.5: High-Accuracy Medical Transcription
Fish Speech V1.5 is a leading open-source text-to-speech (TTS) model employing an innovative DualAR architecture with dual autoregressive transformer design. It supports multiple languages with over 300,000 hours of training data for English and Chinese, and over 100,000 hours for Japanese. In independent evaluations by TTS Arena, the model performed exceptionally well, with an ELO score of 1339. The model achieved a word error rate (WER) of 3.5% and a character error rate (CER) of 1.2% for English, and a CER of 1.3% for Chinese characters, making it highly reliable for healthcare documentation where accuracy is paramount.
Pros
- Exceptional accuracy with 3.5% WER for English medical transcription.
- Multilingual support for diverse healthcare environments.
- Over 300,000 hours of training data ensuring robust performance.
Cons
- Higher pricing at $15/M UTF-8 bytes on SiliconFlow compared to alternatives.
- May require fine-tuning for specific medical terminology.
Why We Love It
- It delivers exceptional accuracy and multilingual capabilities essential for healthcare transcription, with proven performance metrics that meet medical documentation standards.
FunAudioLLM/CosyVoice2-0.5B
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing a unified streaming/non-streaming framework design. The model achieves ultra-low latency of 150ms in streaming mode while maintaining synthesis quality. With a 30%-50% reduction in pronunciation error rate and improved MOS score from 5.4 to 5.53, it supports Chinese dialects, English, Japanese, Korean, and cross-lingual scenarios—perfect for real-time healthcare transcription needs.
FunAudioLLM/CosyVoice2-0.5B: Ultra-Low Latency Medical Streaming
CosyVoice 2 is a streaming speech synthesis model based on a large language model, employing a unified streaming/non-streaming framework design. The model enhances speech token codebook utilization through finite scalar quantization (FSQ) and develops a chunk-aware causal streaming matching model. In streaming mode, it achieves ultra-low latency of 150ms while maintaining synthesis quality almost identical to non-streaming mode. Compared to version 1.0, the pronunciation error rate has been reduced by 30%-50%, the MOS score has improved from 5.4 to 5.53, and it supports fine-grained control over emotions and dialects, making it ideal for real-time healthcare documentation.
Pros
- Ultra-low latency of 150ms for real-time transcription.
- 30%-50% reduction in pronunciation error rate.
- Cost-effective at $7.15/M UTF-8 bytes on SiliconFlow.
Cons
- Smaller 0.5B parameter model may have limitations with complex medical terminology.
- Emotion and dialect controls may not be necessary for clinical applications.
Why We Love It
- It provides ultra-low latency streaming capabilities perfect for real-time healthcare transcription, with significant accuracy improvements and cost-effective pricing on SiliconFlow.
IndexTeam/IndexTTS-2
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model designed for precise duration control in large-scale TTS systems. It supports two modes: explicit token specification for precise duration and free auto-regressive generation. The model achieves disentanglement between emotional expression and speaker identity, incorporates GPT latent representations, and outperforms state-of-the-art zero-shot TTS models in word error rate, speaker similarity, and emotional fidelity—ideal for controlled healthcare documentation scenarios.
IndexTeam/IndexTTS-2: Precision-Controlled Medical Documentation
IndexTTS2 is a breakthrough auto-regressive zero-shot Text-to-Speech model designed to address precise duration control in large-scale TTS systems, a significant advantage for healthcare documentation timing requirements. It introduces a novel method for speech duration control, supporting explicit token specification for precise duration and free auto-regressive generation. The model achieves disentanglement between emotional expression and speaker identity, enabling independent control via separate prompts. To enhance speech clarity, it incorporates GPT latent representations and utilizes a three-stage training paradigm. Experimental results show IndexTTS2 outperforms state-of-the-art zero-shot TTS models in word error rate, speaker similarity, and emotional fidelity across multiple datasets.
Pros
- Precise duration control for timed medical documentation.
- Outperforms state-of-the-art models in word error rate.
- Zero-shot capabilities for immediate deployment.
Cons
- More complex setup due to advanced control features.
- May be over-engineered for simple transcription tasks.
Why We Love It
- It offers unparalleled precision control and superior accuracy metrics, making it perfect for healthcare environments requiring exact timing and high-fidelity medical documentation.
Healthcare Transcription AI Model Comparison
In this table, we compare 2025's leading open-source models for healthcare transcription, each with unique strengths for medical documentation. For high-accuracy multilingual transcription, fishaudio/fish-speech-1.5 provides exceptional precision. For real-time clinical documentation, FunAudioLLM/CosyVoice2-0.5B offers ultra-low latency streaming, while IndexTeam/IndexTTS-2 excels in precision-controlled medical documentation. This side-by-side comparison helps healthcare providers choose the right tool for their specific transcription and documentation needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | fishaudio/fish-speech-1.5 | fishaudio | Text-to-Speech | $15/M UTF-8 bytes | Highest accuracy (3.5% WER) |
| 2 | FunAudioLLM/CosyVoice2-0.5B | FunAudioLLM | Text-to-Speech | $7.15/M UTF-8 bytes | Ultra-low latency (150ms) |
| 3 | IndexTeam/IndexTTS-2 | IndexTeam | Audio | $7.15/M UTF-8 bytes | Precision duration control |
Frequently Asked Questions
Our top three picks for 2025 healthcare transcription are fishaudio/fish-speech-1.5, FunAudioLLM/CosyVoice2-0.5B, and IndexTeam/IndexTTS-2. Each of these models stood out for their accuracy, performance, and unique approach to solving challenges in medical transcription and healthcare documentation.
Our analysis shows different leaders for specific healthcare needs. fishaudio/fish-speech-1.5 is the top choice for highest accuracy medical transcription with its 3.5% WER. For real-time clinical documentation, FunAudioLLM/CosyVoice2-0.5B excels with 150ms latency. For precise timing control in medical documentation, IndexTeam/IndexTTS-2 offers unmatched duration control capabilities.