Fish-Speech 1.4
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Fish Audio
- Organisation type
- Industry
- Country
- United States of America
- Published
- 9 November 2024
- Authors
- Shijia Liao, Yuxuan Wang, Tianyu Li, Yifan Cheng, Ruoyi Zhang, Rongzhi Zhou, Yijin Xing
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech synthesis, Text-to-speech (TTS)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 500,000,000,000 tokens
[tokens] "The dataset contains about 720,000 hours of speech across different languages, with 300,000 hours each of English and Mandarin Chinese as the main components. We also included 20,000 hours each of other language families: Germanic (German), Romance (French, Italian), East Asian (Japanese, Korean), and Semitic (Arabic)." • Batch size: 1M tokens • Training steps: 500K 10^6 * 500000 = 5*10^11 tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.9 × 10²¹ FLOP
- How it was established
- Hardware
"The AR training utilized an 8*H100 80G GPUs for one week, while the vocoder training employed an 8*4090 GPUs for an additional week. Note that these timelines exclude the DPO stage." (989400000000000 FLOP / GPU / sec [H100, bf16 assumed] + 330000000000000 FLOP / GPU / sec [4090, bf16 assumed]) * 8 GPUs * 168 hours * 3600 sec / hour * 0.3 [assumed utilization] = 1.9151355e+21 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H100 SXM5 80GB,NVIDIA GeForce RTX 4090
- Chips used
- 8
- Wall-clock time
- 168 hours (7 days)
"The AR training utilized an 8*H100 80G GPUs for one week, while the vocoder training employed an 8*4090 GPUs for an additional week" 1 week = 168 hours
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (non-commercial)
- Training code
- Open source
- Hugging Face
- fishaudio
This codebase is released under Apache License and all model weights are released under CC-BY-NC-SA-4.0 License https://github.com/fishaudio/fish-speech/tree/main/fish_speech https://huggingface.co/fishaudio/fish-speech-1.4
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Fish-Speech 1.4 was published by Fish Audio, in United States of America, in November 2024. The organisation is categorised as industry.
It works in Speech, and is recorded as doing speech synthesis, Text-to-speech (TTS).
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the fishaudio organisation on Hugging Face.
What went into building it
Training it took roughly 1.9 × 10²¹ FLOP of computation, on NVIDIA H100 SXM5 80GB,NVIDIA GeForce RTX 4090 — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 500,000,000,000 tokens of text.
Answers
Fish-Speech 1.4 — common questions
When was Fish-Speech 1.4 released?
Fish-Speech 1.4 was published in November 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Fish-Speech 1.4 used for?
Fish-Speech 1.4 works in Speech, and is recorded as handling speech synthesis, Text-to-speech (TTS). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Where can I download Fish-Speech 1.4?
Its weights are published under the fishaudio organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train Fish-Speech 1.4?
Around 1.9 × 10²¹ FLOP, on NVIDIA H100 SXM5 80GB,NVIDIA GeForce RTX 4090. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Fish-Speech 1.4?
We cannot say. Fish-Speech 1.4 has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is Fish-Speech 1.4 open source?
Its weights are published, so Fish-Speech 1.4 can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does Fish-Speech 1.4 have?
No parameter count has been published for Fish-Speech 1.4, which is why no memory or speed figure appears on this page.
Who created Fish-Speech 1.4?
Fish-Speech 1.4 was published by Fish Audio, based in United States of America, categorised as industry.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.