This is a demo of Fish Audio's text-to-speech tool using a scripted monologue in Uncle Roger's voice — a character known for critiquing improper cooking techniques. The page lets you generate audio from custom text and adjust playback speed and volume.

• Fish Audio offers a text-to-speech platform with character voice models, demonstrated here with the "Uncle Roger" persona
• The tool includes basic audio controls: volume adjustment, playback speed (1x shown), and character selection via model ID
• Users can input custom text (up to 500 characters) and generate dramatic audio with emotional direction tags like [horrified] and <slow> for pacing

ElevenLabs released Eleven v4, a text-to-speech model that generates emotionally expressive speech by interpreting tone, pacing, and context. It ranks #1 in independent benchmarks and was preferred by ~75% of listeners in blind tests against competitors.

• Eleven v4 can generate speech with distinct emotions (dramatic, tender, urgent, comedic) while maintaining speaker identity, and users can control delivery through natural language prompts and inline tags like [laughs] or [said angrily in French accent]
• Eleven v4 Turbo achieves ~150ms latency for conversational agents, solving the traditional speed-vs-quality tradeoff that forced developers to choose between fast but robotic or slow but expressive voices
• The model supports 90+ languages and can clone voices that speak fluently in any language while adopting native accents and retaining the original voice's identity, improving dubbing and localization work

Google launched two new text-to-speech models that let developers create custom voices from scratch and control delivery line-by-line, with the premium version ranking first on voice customization benchmarks. The models include safeguards like consent verification for voice replication and SynthID watermarking to detect AI-generated speech.

• Gemini 3.8 Flash TTS lets you generate entirely new voices using natural language prompts, customize 2,000+ existing voices, or replicate a voice from a 30-second sample with built-in consent checks.
• Both models rank #1 and #2 on Hume AI's Overall Quality Index and support 100+ languages with strong performance in Japanese, Brazilian Portuguese, Vietnamese, Arabic, Spanish, and Hindi.
• You can direct performance line-by-line with stage directions, maintain voice consistency across hours of audio, stage two-speaker conversations, and add realistic conversational sounds like laughs and sighs.
• Every generated audio is watermarked with SynthID to remain detectable as AI-generated, and the models are rolling out today to developers via Gemini API and Google AI Studio.

StepAudio 3 Gen is a new audio generation model that handles text-to-speech, voice design, sound effects, music, and speech all in one system using discrete autoregressive modeling instead of the diffusion approach most competitors use. It tokenizes audio at 12.5 Hz and generates by predicting codebooks sequentially, achieving state-of-the-art results on TTS and voice design tasks.

• Uses residual vector quantization tokens with a 16×2048 codebook space that jointly encodes semantic and waveform information, letting each layer preserve both types of data
• Employs a two-stage generation process: autoregressive prediction along the time axis for the first codebook, then a lightweight causal Transformer fills in the remaining 15 codebooks along the codebook axis
• Applies interference-aware progressive pretraining to add audio capabilities without degrading the underlying language model's text abilities

Fish Audio’s open-source voice-generation models now serve over 8 million users and deliver $21 million in ARR. The startup secured $52 million in seed funding to expand its expressive, steerable voice APIs for both creators and enterprises, while streamlining takedowns after consent disputes. It plans to launch new audio-understanding and speech-to-speech models later this year.

• Fish Audio raised $52M in seed funding while already generating $21M ARR from 8 million users—unusually mature metrics for a seed-stage company.
• The startup automated voice takedowns (under 3 minutes via sample or contract) after facing unauthorized upload disputes, betting that consent and fair licensing build trust with creators.
• Its open-source Fish Speech release hit 31,000+ GitHub stars, but the newest S2.1 Pro model is locked behind a paid API, marking a shift from open-source to monetized enterprise offerings.
• It plans to expand into audio-understanding and speech-to-speech models to compete with ElevenLabs, WellSaid, and Speechify on fine-grained control and cheaper training costs.