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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=mtg/maest
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01",
{
input: {
audio: "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac",
top_n: 10,
output_format: "Visualization"
}
}
);
// 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 mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01",
input={
"audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac",
"top_n": 10,
"output_format": "Visualization"
}
)
print(output)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 mtg/maest 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": "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01",
"input": {
"audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac",
"top_n": 10,
"output_format": "Visualization"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
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Output
{
"completed_at": "2023-11-04T16:51:22.493432Z",
"created_at": "2023-11-04T16:48:24.038840Z",
"data_removed": false,
"error": null,
"id": "xovtf4rbbgwydhjlkuf33g2dn4",
"input": {
"audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac",
"top_n": 10,
"output_format": "Visualization"
},
"logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 16:50:54.787371: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz\n2023-11-04 16:51:00.648102: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327\n2023-11-04 16:51:09.785253: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n2023-11-04 16:51:11.289404: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101\n2023-11-04 16:51:16.034519: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n2023-11-04 16:51:18.479632: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n2023-11-04 16:51:21.498935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.500252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 16:51:21.500957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.502372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.503392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n2023-11-04 16:51:21.503432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 16:51:21.503441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 16:51:21.503449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n2023-11-04 16:51:21.503608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.504818: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.505847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!",
"metrics": {
"predict_time": 29.198043,
"total_time": 178.454592
},
"output": "https://replicate.delivery/pbxt/wXZQcGdcqypFG9aeyQ0XZTSruzMVX0h4yMt9YYed6RmKiC1RA/out.png",
"started_at": "2023-11-04T16:50:53.295389Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/xovtf4rbbgwydhjlkuf33g2dn4",
"cancel": "https://api.replicate.com/v1/predictions/xovtf4rbbgwydhjlkuf33g2dn4/cancel"
},
"version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01"
}Generated in
loading audio...
running the model...
plotting...
2023-11-04 16:50:54.787371: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz
2023-11-04 16:51:00.648102: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327
2023-11-04 16:51:09.785253: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2023-11-04 16:51:11.289404: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101
2023-11-04 16:51:16.034519: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2023-11-04 16:51:18.479632: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2023-11-04 16:51:21.498935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-11-04 16:51:21.500252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s
2023-11-04 16:51:21.500957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-11-04 16:51:21.502372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-11-04 16:51:21.503392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
2023-11-04 16:51:21.503432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-11-04 16:51:21.503441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0
2023-11-04 16:51:21.503449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N
2023-11-04 16:51:21.503608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-11-04 16:51:21.504818: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-11-04 16:51:21.505847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
done!