Evidence Receipt. Related Resources.
Fish Audio S2 Technical Report
Compared to this week’s papers
Verification pending
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Use Signal Canvas as the narrative proof surface
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Use This Via API or MCP
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Route this paper proof surface into REST, MCP, or developer workflows while preserving the same evidence receipt and related-resource context.
Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/fish-audio-s2-technical-report
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 9/10
- Last proof check
- 2026-03-19
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 33%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Fish Audio S2 Technical Report
Canonical ID fish-audio-s2-technical-report | Route /signal-canvas/fish-audio-s2-technical-report
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/fish-audio-s2-technical-reportMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "fish-audio-s2-technical-report",
"query_text": "Summarize Fish Audio S2 Technical Report"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Fish Audio S2 Technical Report",
"normalized_query": "2603.08823",
"route": "/signal-canvas/fish-audio-s2-technical-report",
"paper_ref": "fish-audio-s2-technical-report",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Evidence Receipt
Preparing verified analysis
Dimensions overall score 9.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
The inference engine is production-ready for streaming, achieving an RTF of 0.195 and a time-to-first-audio below 100 ms.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
To scale training, we develop a multi-stage training recipe together with a staged data pipeline covering video captioning and speech captioning, voice-quality assessment, and reward modeling.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
The inference engine is production-ready for streaming, achieving an RTF of 0.195 and a time-to-first-audio below 100 ms.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
To push the frontier of open-source TTS, we release our model weights, fine-tuning code, and an SGLang-based inference engine.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions.
ImplicationpartialDirectly stated in the abstract with clear description of features.
Verificationpartialpartial
- Evidencepartial
we develop a multi-stage training recipe together with a staged data pipeline covering video captioning and speech captioning, voice-quality assessment, and reward modeling.
ImplicationpartialExplicitly stated in the abstract as part of the training approach.
Verificationpartialpartial
- Evidencepartial
achieving an RTF of 0.195 and a time-to-first-audio below 100 ms.
ImplicationpartialDirectly stated with specific numeric values in the abstract.
Verificationpartialpartial
- Evidencepartial
The inference engine is production-ready for streaming
ImplicationpartialExplicitly stated in the abstract, though 'production-ready' is somewhat subjective.
Verificationpartialpartial
Startup potential card
Related Resources
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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