Prediction

Model

ttsds/fishspeech_1_1_large:bf25b860

z4ex31txjdrme0cmq0kv4mm6b0

Status

Succeeded

Source

Web

Hardware

L40S

Total duration

1m 55s

Created

11 months ago

Webhook

–

Input

text: With tenure, Suzie'd have all the more leisure for yachting, but her publications are no good.
speaker_reference: https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav
text_reference: and keeping eternity before the eyes, though much.

{
  "speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav",
  "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."
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_dRL**********************************

This is your API token. Keep it to yourself.

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: {
      speaker_reference: "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav",
      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."
    }
  }
);

// 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 replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_dRL**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run 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={
        "speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav",
        "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."
    }
)

# 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=r8_dRL**********************************

This is your API token. Keep it to yourself.

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": {
      "speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav",
      "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."
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "z4ex31txjdrme0cmq0kv4mm6b0",
  "model": "ttsds/fishspeech_1_1_large",
  "version": "bf25b86020c83763b6727b138d7b0c3308dd210ee059accc00cb6f1971bbcd37",
  "input": {
    "speaker_reference": "https://replicate.delivery/pbxt/MNFXdPaUPOwYCZjZM4azsymbzE2TCV2WJXfGpeV2DrFWaSq8/example_en.wav",
    "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."
  },
  "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",
  "output": "https://replicate.delivery/xezq/cvDmYj7AkS7tCJ1L4xokpE0UPfq8qIr2Ey17B5YYk2fqJWKUA/generated.wav",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2025-01-30T16:59:35.187Z",
  "started_at": "2025-01-30T17:01:25.804994Z",
  "completed_at": "2025-01-30T17:01:30.241508Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/z4ex31txjdrme0cmq0kv4mm6b0/cancel",
    "get": "https://api.replicate.com/v1/predictions/z4ex31txjdrme0cmq0kv4mm6b0",
    "stream": "https://stream.replicate.com/v1/files/bsvm-oyur2w3altprrqoq2gmfap2fatjfjob7q4ccipws6k27y6glqlda",
    "web": "https://replicate.com/p/z4ex31txjdrme0cmq0kv4mm6b0"
  },
  "metrics": {
    "predict_time": 4.436514462,
    "total_time": 115.054508
  }
}

Generated in

4.4 seconds

Tweak it Report

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] 1%|▏ | 24/1857 [00:00<00:33, 54.77it/s] 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] 0%| | 6/1726 [00:00<00:30, 55.57it/s] 1%| | 12/1726 [00:00<00:30, 55.47it/s] 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] 2%|▏ | 36/1726 [00:00<00:30, 55.52it/s] 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] 0%| | 6/1629 [00:00<00:29, 55.72it/s] 1%| | 12/1629 [00:00<00:29, 55.44it/s] 1%| | 18/1629 [00:00<00:29, 55.43it/s] 1%|▏ | 24/1629 [00:00<00:28, 55.44it/s] 2%|▏ | 30/1629 [00:00<00:28, 55.51it/s] 2%|▏ | 36/1629 [00:00<00:28, 55.53it/s] 3%|▎ | 42/1629 [00:00<00:28, 55.52it/s] 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] 0%| | 6/1561 [00:00<00:28, 54.37it/s] 1%| | 12/1561 [00:00<00:28, 55.05it/s] 1%| | 18/1561 [00:00<00:28, 54.63it/s] 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 sample