Real-Time Multilingual Voice Translation
Automatic Speech Translation with Sub-Second Latency
Healthcare and enterprise clients require real-time multilingual support.
Arabic BLEU
48.19
CoVoST2
S2T Latency
0.763s
Seamless
Executive Summary
What we built
A comprehensive Automatic Speech Translation evaluation and deployment framework comparing open-source and proprietary models across multiple language pairs and datasets.
Why it matters
Healthcare and enterprise clients require real-time multilingual support. Model selection requires empirical benchmarks on domain-relevant data, and latency vs. quality tradeoffs must be quantified for production decisions.
Results
- Spanish BLEU 45.09 with Voxtral-Small-24B on CoVoST2
- Arabic BLEU 48.19 with Seamless-m4t-v2-large
- Sub-second latency (0.763s) for S2T translation
- Benchmarked Spanish, Arabic, Russian, and Cantonese
Best for
- →Healthcare multilingual support
- →Real-time voice translation
- →Enterprise global deployments
- →Low-latency translation applications
Limitations
- Cantonese support still developing (Seamless has generation errors)
- Proprietary models have higher latency (1.8-2.3s)
- Quality varies by model and language pair
How It Works
A two-layer detection system where each covers the other's weaknesses.
Speech-to-Text (S2T)
Direct speech to translated text
- Lower latency than traditional pipeline
- Best for applications with TTS step
- Seamless-m4t-v2-large achieves 0.763s
Speech-to-Speech (S2S)
End-to-end speech translation
- Lowest latency possible
- Preserves speaker characteristics
- Less controllable output
Traditional Pipeline
ASR → MT/LLM → TTS
- Modular and debuggable
- Higher latency due to multiple steps
- Error can propagate through pipeline
Benchmark Results
Interactive explorer comparing Anyreach to baseline methods across datasets and metrics.
Baseline Comparison: Accuracy (%)
| Name | Region | accuracy | precision | recall | f1 | latency | Samples |
|---|---|---|---|---|---|---|---|
Seamless-m4t-v2-large | Russian | 53.96% | 88.94% | 87% | 87.9 | 763ms | 10,000±0.7 |
Seamless-m4t-v2-large | Arabic | 48.19% | 88.9% | 86% | 87.4 | 763ms | 10,000±0.6 |
Voxtral-Small-24B | Spanish | 45.09% | 87.67% | 85% | 86.3 | 1034ms | 10,000±0.6 |
Product Features
Ready for production with enterprise-grade reliability.
Sub-Second Latency
Seamless-m4t-v2-large achieves 0.763s average latency for speech-to-text translation.
Multiple Language Pairs
Benchmarked Spanish, Arabic, Russian, and Cantonese with production-ready metrics.
Model Flexibility
Choose between quality (Voxtral), latency (Seamless), or cost (Canary) optimized models.
Comprehensive Metrics
BLEU for syntactic quality, COMET for semantic quality, chrF++ for character-level accuracy.
Baseten Deployment
Production-ready infrastructure with Seamless, Canary, and Voxtral models deployed.
Concurrency Support
Canary-1b-v2 handles 32 concurrent requests at 5.09/s throughput.
Integration Details
Runs On
Baseten Infrastructure
Latency Budget
<1s for S2T, <2s for full pipeline
Providers
Seamless-m4t-v2-large, Canary-1b-v2, Voxtral-Small-24B
Implementation
1-2 weeks for production deployment
Frequently Asked Questions
Common questions about our voicemail detection system.
Ready to see this in action?
Book a technical walkthrough with our team to see how this research applies to your use case.