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ttsds/fishspeech_1_1_large
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fishspeech_1_1_large
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ttsds/amphion_valle
The VALL-E models by Amphion.
ttsds/amphion_naturalspeech2
The NaturalSpeech2 model by Amphion.
ttsds/amphion_maskgct
The MaskGCT model by Amphion.
ttsds/amphion_vevo
The Vevo model by Amphion.
ttsds/bark
The Bark model by Suno.
ttsds/bark_small
The small version of the Bark model by Suno.
ttsds/e2
ttsds/fishspeech_1_0
The Fish Speech V1.0 model.
ttsds/fishspeech_1_1
The Fish Speech V1.1 model.
ttsds/fishspeech_1_2
The Fish Speech V1.2 model.
ttsds/fishspeech_1_2_sft
The Fish Speech V1.2 SFT model.
ttsds/fishspeech_1_4
The Fish Speech V1.4 model.
ttsds/fishspeech_1_5
The Fish Speech V1.5 model.
ttsds/gptsovits_1
ttsds/gptsovits_2
ttsds/hierspeechpp_lt460
ttsds/hierspeechpp_1
ttsds/hierspeechpp_1_1
ttsds/f5
ttsds/metavoice
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=ttsds/fishspeech_1_1_large
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
import fs from "node:fs";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run ttsds/fishspeech_1_1_large using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"ttsds/fishspeech_1_1_large:bf25b86020c83763b6727b138d7b0c3308dd210ee059accc00cb6f1971bbcd37",
{
input: {
text: "With tenure, Suzie'd have all the more leisure for yachting, but her publications are no good.",
text_reference: "and keeping eternity before the eyes, though much.",
speaker_reference: "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav"
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun ttsds/fishspeech_1_1_large using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"ttsds/fishspeech_1_1_large:bf25b86020c83763b6727b138d7b0c3308dd210ee059accc00cb6f1971bbcd37",
input={
"text": "With tenure, Suzie'd have all the more leisure for yachting, but her publications are no good.",
"text_reference": "and keeping eternity before the eyes, though much.",
"speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav"
}
)
# To access the file URL:
print(output.url())
#=> "http://example.com"
# To write the file to disk:
with open("my-image.png", "wb") as file:
file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run ttsds/fishspeech_1_1_large using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
-H "Content-Type: application/json" \
-H "Prefer: wait" \
-d $'{
"version": "ttsds/fishspeech_1_1_large:bf25b86020c83763b6727b138d7b0c3308dd210ee059accc00cb6f1971bbcd37",
"input": {
"text": "With tenure, Suzie\'d have all the more leisure for yachting, but her publications are no good.",
"text_reference": "and keeping eternity before the eyes, though much.",
"speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
Video Player is loading.
Current Time 00:00:000
/
Duration 00:00:000
Loaded: 0%
00:00:000
Stream Type LIVE
Remaining Time -00:00:000
1x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
{
"completed_at": "2025-01-30T17:01:30.241508Z",
"created_at": "2025-01-30T16:59:35.187000Z",
"data_removed": false,
"error": null,
"id": "z4ex31txjdrme0cmq0kv4mm6b0",
"input": {
"text": "With tenure, Suzie'd have all the more leisure for yachting, but her publications are no good.",
"text_reference": "and keeping eternity before the eyes, though much.",
"speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav"
},
"logs": "2025-01-30 17:01:26.111 | INFO | tools.llama.generate:generate_long:491 - Encoded text: With tenure, Suzie'd have all\n2025-01-30 17:01:26.111 | INFO | tools.llama.generate:generate_long:491 - Encoded text: the more leisure for yachting,\n2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:491 - Encoded text: but her publications are no\n2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:491 - Encoded text: good.\n2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 1/4 of sample 1/1\n 0%| | 0/1857 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/backends/cuda/__init__.py:342: FutureWarning: torch.backends.cuda.sdp_kernel() is deprecated. In the future, this context manager will be removed. Please see, torch.nn.attention.sdpa_kernel() for the new context manager, with updated signature.\nwarnings.warn(\n 0%| | 6/1857 [00:00<00:35, 51.79it/s]\n 1%| | 12/1857 [00:00<00:34, 53.93it/s]\n 1%| | 18/1857 [00:00<00:33, 54.19it/s]\n 1%|▏ | 24/1857 [00:00<00:33, 54.77it/s]\n 2%|▏ | 30/1857 [00:00<00:33, 55.09it/s]\n 2%|▏ | 36/1857 [00:00<00:32, 55.26it/s]\n 2%|▏ | 42/1857 [00:00<00:32, 55.42it/s]\n 3%|▎ | 48/1857 [00:00<00:32, 55.49it/s]\n 3%|▎ | 54/1857 [00:00<00:32, 55.56it/s]\n 3%|▎ | 60/1857 [00:01<00:32, 55.55it/s]\n 4%|▎ | 66/1857 [00:01<00:32, 55.52it/s]\n 4%|▍ | 72/1857 [00:01<00:32, 55.56it/s]\n 4%|▍ | 78/1857 [00:01<00:32, 55.21it/s]\n4%|▍ | 81/1857 [00:01<00:32, 54.48it/s]\n2025-01-30 17:01:27.699 | INFO | tools.llama.generate:generate_long:565 - Generated 83 tokens in 1.59 seconds, 52.28 tokens/sec\n2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 53.67 GB/s\n2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB\n2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 2/4 of sample 1/1\n 0%| | 0/1726 [00:00<?, ?it/s]\n 0%| | 6/1726 [00:00<00:30, 55.57it/s]\n 1%| | 12/1726 [00:00<00:30, 55.47it/s]\n 1%| | 18/1726 [00:00<00:30, 55.52it/s]\n 1%|▏ | 24/1726 [00:00<00:30, 55.57it/s]\n 2%|▏ | 30/1726 [00:00<00:30, 55.51it/s]\n 2%|▏ | 36/1726 [00:00<00:30, 55.52it/s]\n 2%|▏ | 42/1726 [00:00<00:30, 55.54it/s]\n 3%|▎ | 48/1726 [00:00<00:30, 55.53it/s]\n3%|▎ | 50/1726 [00:00<00:30, 54.43it/s]\n2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:565 - Generated 52 tokens in 0.94 seconds, 55.04 tokens/sec\n2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 56.50 GB/s\n2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB\n2025-01-30 17:01:28.646 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 3/4 of sample 1/1\n 0%| | 0/1629 [00:00<?, ?it/s]\n 0%| | 6/1629 [00:00<00:29, 55.72it/s]\n 1%| | 12/1629 [00:00<00:29, 55.44it/s]\n 1%| | 18/1629 [00:00<00:29, 55.43it/s]\n 1%|▏ | 24/1629 [00:00<00:28, 55.44it/s]\n 2%|▏ | 30/1629 [00:00<00:28, 55.51it/s]\n 2%|▏ | 36/1629 [00:00<00:28, 55.53it/s]\n 3%|▎ | 42/1629 [00:00<00:28, 55.52it/s]\n3%|▎ | 43/1629 [00:00<00:29, 54.22it/s]\n2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:565 - Generated 45 tokens in 0.81 seconds, 55.34 tokens/sec\n2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 56.81 GB/s\n2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB\n2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 4/4 of sample 1/1\n 0%| | 0/1561 [00:00<?, ?it/s]\n 0%| | 6/1561 [00:00<00:28, 54.37it/s]\n 1%| | 12/1561 [00:00<00:28, 55.05it/s]\n 1%| | 18/1561 [00:00<00:28, 54.63it/s]\n1%|▏ | 22/1561 [00:00<00:29, 52.42it/s]\n2025-01-30 17:01:29.899 | INFO | tools.llama.generate:generate_long:565 - Generated 24 tokens in 0.44 seconds, 54.53 tokens/sec\n2025-01-30 17:01:29.900 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 55.97 GB/s\n2025-01-30 17:01:29.900 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB\n/root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/nn/modules/conv.py:306: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\nreturn F.conv1d(input, weight, bias, self.stride,\nNext sample",
"metrics": {
"predict_time": 4.436514462,
"total_time": 115.054508
},
"output": "https://replicate.delivery/xezq/cvDmYj7AkS7tCJ1L4xokpE0UPfq8qIr2Ey17B5YYk2fqJWKUA/generated.wav",
"started_at": "2025-01-30T17:01:25.804994Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/bsvm-oyur2w3altprrqoq2gmfap2fatjfjob7q4ccipws6k27y6glqlda",
"get": "https://api.replicate.com/v1/predictions/z4ex31txjdrme0cmq0kv4mm6b0",
"cancel": "https://api.replicate.com/v1/predictions/z4ex31txjdrme0cmq0kv4mm6b0/cancel"
},
"version": "bf25b86020c83763b6727b138d7b0c3308dd210ee059accc00cb6f1971bbcd37"
}Generated in
2025-01-30 17:01:26.111 | INFO | tools.llama.generate:generate_long:491 - Encoded text: With tenure, Suzie'd have all
2025-01-30 17:01:26.111 | INFO | tools.llama.generate:generate_long:491 - Encoded text: the more leisure for yachting,
2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:491 - Encoded text: but her publications are no
2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:491 - Encoded text: good.
2025-01-30 17:01:26.112 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 1/4 of sample 1/1
0%| | 0/1857 [00:00<?, ?it/s]/root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/backends/cuda/__init__.py:342: FutureWarning: torch.backends.cuda.sdp_kernel() is deprecated. In the future, this context manager will be removed. Please see, torch.nn.attention.sdpa_kernel() for the new context manager, with updated signature.
warnings.warn(
0%| | 6/1857 [00:00<00:35, 51.79it/s]
1%| | 12/1857 [00:00<00:34, 53.93it/s]
1%| | 18/1857 [00:00<00:33, 54.19it/s]
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2%|▏ | 30/1857 [00:00<00:33, 55.09it/s]
2%|▏ | 36/1857 [00:00<00:32, 55.26it/s]
2%|▏ | 42/1857 [00:00<00:32, 55.42it/s]
3%|▎ | 48/1857 [00:00<00:32, 55.49it/s]
3%|▎ | 54/1857 [00:00<00:32, 55.56it/s]
3%|▎ | 60/1857 [00:01<00:32, 55.55it/s]
4%|▎ | 66/1857 [00:01<00:32, 55.52it/s]
4%|▍ | 72/1857 [00:01<00:32, 55.56it/s]
4%|▍ | 78/1857 [00:01<00:32, 55.21it/s]
4%|▍ | 81/1857 [00:01<00:32, 54.48it/s]
2025-01-30 17:01:27.699 | INFO | tools.llama.generate:generate_long:565 - Generated 83 tokens in 1.59 seconds, 52.28 tokens/sec
2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 53.67 GB/s
2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB
2025-01-30 17:01:27.700 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 2/4 of sample 1/1
0%| | 0/1726 [00:00<?, ?it/s]
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1%| | 18/1726 [00:00<00:30, 55.52it/s]
1%|▏ | 24/1726 [00:00<00:30, 55.57it/s]
2%|▏ | 30/1726 [00:00<00:30, 55.51it/s]
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2%|▏ | 42/1726 [00:00<00:30, 55.54it/s]
3%|▎ | 48/1726 [00:00<00:30, 55.53it/s]
3%|▎ | 50/1726 [00:00<00:30, 54.43it/s]
2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:565 - Generated 52 tokens in 0.94 seconds, 55.04 tokens/sec
2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 56.50 GB/s
2025-01-30 17:01:28.645 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB
2025-01-30 17:01:28.646 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 3/4 of sample 1/1
0%| | 0/1629 [00:00<?, ?it/s]
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3%|▎ | 43/1629 [00:00<00:29, 54.22it/s]
2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:565 - Generated 45 tokens in 0.81 seconds, 55.34 tokens/sec
2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 56.81 GB/s
2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB
2025-01-30 17:01:29.459 | INFO | tools.llama.generate:generate_long:509 - Generating sentence 4/4 of sample 1/1
0%| | 0/1561 [00:00<?, ?it/s]
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1%|▏ | 22/1561 [00:00<00:29, 52.42it/s]
2025-01-30 17:01:29.899 | INFO | tools.llama.generate:generate_long:565 - Generated 24 tokens in 0.44 seconds, 54.53 tokens/sec
2025-01-30 17:01:29.900 | INFO | tools.llama.generate:generate_long:568 - Bandwidth achieved: 55.97 GB/s
2025-01-30 17:01:29.900 | INFO | tools.llama.generate:generate_long:573 - GPU Memory used: 4.01 GB
/root/.pyenv/versions/3.11.10/lib/python3.11/site-packages/torch/nn/modules/conv.py:306: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
return F.conv1d(input, weight, bias, self.stride,
Next sampleRun time and cost
This model costs approximately $0.0044 to run on Replicate, or 227 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 5 seconds.