cjwbw / melotts
High-quality multilingual text-to-speech library
Prediction
cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688ID3rnywpbbdxsdal2q2bpswvxk2qStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- The field of text-to-speech has seen rapid development recently.
- speed
- 1
- speaker
- EN-US
- language
- EN
{ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", { input: { text: "The field of text-to-speech has seen rapid development recently.", speed: 1, speaker: "EN-US", language: "EN" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", input={ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-02T23:27:49.033396Z", "created_at": "2024-03-02T23:22:43.313894Z", "data_removed": false, "error": null, "id": "3rnywpbbdxsdal2q2bpswvxk2q", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" }, "logs": "> Text split to sentences.\nThe field of text-to-speech has seen rapid development recently.\n> ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\nmodel.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]\u001b[A\nmodel.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 88.9MB/s]\u001b[A\nmodel.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 140MB/s] \u001b[A\nmodel.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 156MB/s]\u001b[A\nmodel.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 165MB/s]\u001b[A\nmodel.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 170MB/s]\u001b[A\nmodel.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 176MB/s] \u001b[A\nmodel.safetensors: 31%|███ | 136M/440M [00:00<00:01, 174MB/s]\u001b[A\nmodel.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 178MB/s]\u001b[A\nmodel.safetensors: 40%|████ | 178M/440M [00:01<00:01, 180MB/s]\u001b[A\nmodel.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s]\u001b[A\nmodel.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s]\u001b[A\nmodel.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 184MB/s]\u001b[A\nmodel.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 186MB/s]\u001b[A\nmodel.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 182MB/s]\u001b[A\nmodel.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 180MB/s]\u001b[A\nmodel.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 179MB/s]\u001b[A\nmodel.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 178MB/s]\u001b[A\nmodel.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 177MB/s]\u001b[A\nmodel.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 178MB/s]\u001b[A\nmodel.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 177MB/s]\u001b[A\nmodel.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 174MB/s]\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n100%|██████████| 1/1 [00:11<00:00, 11.57s/it]\n100%|██████████| 1/1 [00:11<00:00, 11.57s/it]", "metrics": { "predict_time": 11.973009, "total_time": 305.719502 }, "output": "https://replicate.delivery/pbxt/8IZENsNV7Sr1AJaLbWz2orEbKKmSbqzCh1uAEldmw6B9nFnE/out.wav", "started_at": "2024-03-02T23:27:37.060387Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3rnywpbbdxsdal2q2bpswvxk2q", "cancel": "https://api.replicate.com/v1/predictions/3rnywpbbdxsdal2q2bpswvxk2q/cancel" }, "version": "ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688" }Generated in> Text split to sentences. The field of text-to-speech has seen rapid development recently. > =========================== 0%| | 0/1 [00:00<?, ?it/s] model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s] model.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 88.9MB/s] model.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 140MB/s] model.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 156MB/s] model.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 165MB/s] model.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 170MB/s] model.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 176MB/s] model.safetensors: 31%|███ | 136M/440M [00:00<00:01, 174MB/s] model.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 178MB/s] model.safetensors: 40%|████ | 178M/440M [00:01<00:01, 180MB/s] model.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s] model.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s] model.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 184MB/s] model.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 186MB/s] model.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s] model.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 182MB/s] model.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 180MB/s] model.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 179MB/s] model.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 178MB/s] model.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 177MB/s] model.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 178MB/s] model.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 177MB/s] model.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 174MB/s] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 100%|██████████| 1/1 [00:11<00:00, 11.57s/it] 100%|██████████| 1/1 [00:11<00:00, 11.57s/it]Prediction
cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688ID3rnywpbbdxsdal2q2bpswvxk2qStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- The field of text-to-speech has seen rapid development recently.
- speed
- 1
- speaker
- EN-US
- language
- EN
{ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", { input: { text: "The field of text-to-speech has seen rapid development recently.", speed: 1, speaker: "EN-US", language: "EN" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", input={ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-02T23:27:49.033396Z", "created_at": "2024-03-02T23:22:43.313894Z", "data_removed": false, "error": null, "id": "3rnywpbbdxsdal2q2bpswvxk2q", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-US", "language": "EN" }, "logs": "> Text split to sentences.\nThe field of text-to-speech has seen rapid development recently.\n> ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\nmodel.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]\u001b[A\nmodel.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 88.9MB/s]\u001b[A\nmodel.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 140MB/s] \u001b[A\nmodel.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 156MB/s]\u001b[A\nmodel.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 165MB/s]\u001b[A\nmodel.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 170MB/s]\u001b[A\nmodel.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 176MB/s] \u001b[A\nmodel.safetensors: 31%|███ | 136M/440M [00:00<00:01, 174MB/s]\u001b[A\nmodel.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 178MB/s]\u001b[A\nmodel.safetensors: 40%|████ | 178M/440M [00:01<00:01, 180MB/s]\u001b[A\nmodel.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s]\u001b[A\nmodel.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s]\u001b[A\nmodel.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 184MB/s]\u001b[A\nmodel.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 186MB/s]\u001b[A\nmodel.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 182MB/s]\u001b[A\nmodel.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 180MB/s]\u001b[A\nmodel.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 179MB/s]\u001b[A\nmodel.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 178MB/s]\u001b[A\nmodel.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 177MB/s]\u001b[A\nmodel.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 178MB/s]\u001b[A\nmodel.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 177MB/s]\u001b[A\nmodel.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 174MB/s]\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n100%|██████████| 1/1 [00:11<00:00, 11.57s/it]\n100%|██████████| 1/1 [00:11<00:00, 11.57s/it]", "metrics": { "predict_time": 11.973009, "total_time": 305.719502 }, "output": "https://replicate.delivery/pbxt/8IZENsNV7Sr1AJaLbWz2orEbKKmSbqzCh1uAEldmw6B9nFnE/out.wav", "started_at": "2024-03-02T23:27:37.060387Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3rnywpbbdxsdal2q2bpswvxk2q", "cancel": "https://api.replicate.com/v1/predictions/3rnywpbbdxsdal2q2bpswvxk2q/cancel" }, "version": "ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688" }Generated in> Text split to sentences. The field of text-to-speech has seen rapid development recently. > =========================== 0%| | 0/1 [00:00<?, ?it/s] model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s] model.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 88.9MB/s] model.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 140MB/s] model.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 156MB/s] model.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 165MB/s] model.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 170MB/s] model.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 176MB/s] model.safetensors: 31%|███ | 136M/440M [00:00<00:01, 174MB/s] model.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 178MB/s] model.safetensors: 40%|████ | 178M/440M [00:01<00:01, 180MB/s] model.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s] model.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s] model.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 184MB/s] model.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 186MB/s] model.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s] model.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 182MB/s] model.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 180MB/s] model.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 179MB/s] model.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 178MB/s] model.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 177MB/s] model.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 178MB/s] model.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 177MB/s] model.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 174MB/s] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 100%|██████████| 1/1 [00:11<00:00, 11.57s/it] 100%|██████████| 1/1 [00:11<00:00, 11.57s/it]Prediction
cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688IDcqvpx3rb2vatlugqcsyqyqy7oiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- The field of text-to-speech has seen rapid development recently.
- speed
- 1
- speaker
- EN-BR
- language
- EN
{ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", { input: { text: "The field of text-to-speech has seen rapid development recently.", speed: 1, speaker: "EN-BR", language: "EN" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", input={ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-02T23:47:48.277502Z", "created_at": "2024-03-02T23:44:14.898677Z", "data_removed": false, "error": null, "id": "cqvpx3rb2vatlugqcsyqyqy7oi", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }, "logs": "> Text split to sentences.\nThe field of text-to-speech has seen rapid development recently.\n > ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\nmodel.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]\u001b[A\nmodel.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 93.2MB/s]\u001b[A\nmodel.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 141MB/s] \u001b[A\nmodel.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 157MB/s]\u001b[A\nmodel.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 162MB/s]\u001b[A\nmodel.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 167MB/s]\u001b[A\nmodel.safetensors: 26%|██▌ | 115M/440M [00:00<00:02, 161MB/s] \u001b[A\nmodel.safetensors: 31%|███ | 136M/440M [00:00<00:01, 164MB/s]\u001b[A\nmodel.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 167MB/s]\u001b[A\nmodel.safetensors: 40%|████ | 178M/440M [00:01<00:01, 170MB/s]\u001b[A\nmodel.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 168MB/s]\u001b[A\nmodel.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 169MB/s]\u001b[A\nmodel.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 171MB/s]\u001b[A\nmodel.safetensors: 60%|█████▉ | 262M/440M [00:01<00:01, 174MB/s]\u001b[A\nmodel.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 176MB/s]\u001b[A\nmodel.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 177MB/s]\u001b[A\nmodel.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 176MB/s]\u001b[A\nmodel.safetensors: 79%|███████▊ | 346M/440M [00:02<00:00, 169MB/s]\u001b[A\nmodel.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 172MB/s]\u001b[A\nmodel.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 172MB/s]\u001b[A\nmodel.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 174MB/s]\u001b[A\nmodel.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 174MB/s]\u001b[A\nmodel.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 168MB/s]\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n100%|██████████| 1/1 [00:18<00:00, 18.50s/it]\n100%|██████████| 1/1 [00:18<00:00, 18.50s/it]", "metrics": { "predict_time": 19.172517, "total_time": 213.378825 }, "output": "https://replicate.delivery/pbxt/vTviWSu9oho3DtYEsYPvA3SI1XPXKEJNzIIDvQADulIpsFnE/out.wav", "started_at": "2024-03-02T23:47:29.104985Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cqvpx3rb2vatlugqcsyqyqy7oi", "cancel": "https://api.replicate.com/v1/predictions/cqvpx3rb2vatlugqcsyqyqy7oi/cancel" }, "version": "ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688" }Generated in> Text split to sentences. The field of text-to-speech has seen rapid development recently. > =========================== 0%| | 0/1 [00:00<?, ?it/s] model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s] model.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 93.2MB/s] model.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 141MB/s] model.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 157MB/s] model.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 162MB/s] model.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 167MB/s] model.safetensors: 26%|██▌ | 115M/440M [00:00<00:02, 161MB/s] model.safetensors: 31%|███ | 136M/440M [00:00<00:01, 164MB/s] model.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 167MB/s] model.safetensors: 40%|████ | 178M/440M [00:01<00:01, 170MB/s] model.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 168MB/s] model.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 169MB/s] model.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 171MB/s] model.safetensors: 60%|█████▉ | 262M/440M [00:01<00:01, 174MB/s] model.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 176MB/s] model.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 177MB/s] model.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 176MB/s] model.safetensors: 79%|███████▊ | 346M/440M [00:02<00:00, 169MB/s] model.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 172MB/s] model.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 172MB/s] model.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 174MB/s] model.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 174MB/s] model.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 168MB/s] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 100%|██████████| 1/1 [00:18<00:00, 18.50s/it] 100%|██████████| 1/1 [00:18<00:00, 18.50s/it]Prediction
cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6fIDvgvrgobbfp77oizo6x523fpvsiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- テキスト読み上げの分野は最近急速な発展を遂げています
- speed
- 1
- speaker
- -
- language
- JP
{ "text": "テキスト読み上げの分野は最近急速な発展を遂げています", "speed": 1, "speaker": "-", "language": "JP" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", { input: { text: "テキスト読み上げの分野は最近急速な発展を遂げています", speed: 1, speaker: "-", language: "JP" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", input={ "text": "テキスト読み上げの分野は最近急速な発展を遂げています", "speed": 1, "speaker": "-", "language": "JP" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", "input": { "text": "テキスト読み上げの分野は最近急速な発展を遂げています", "speed": 1, "speaker": "-", "language": "JP" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-03T01:09:45.658722Z", "created_at": "2024-03-03T01:06:38.974087Z", "data_removed": false, "error": null, "id": "vgvrgobbfp77oizo6x523fpvsi", "input": { "text": "テキスト読み上げの分野は最近急速な発展を遂げています", "speed": 1, "speaker": "-", "language": "JP" }, "logs": "> Text split to sentences.\nテキスト読み上げの分野は最近急速な発展を遂げています\n> ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:03<00:00, 3.28s/it]\n100%|██████████| 1/1 [00:03<00:00, 3.28s/it]", "metrics": { "predict_time": 3.634032, "total_time": 186.684635 }, "output": "https://replicate.delivery/pbxt/9Xe65Rm5pQ1CdCD6jWjA4iJyqA7jqyYfReMcSgmn7krzefiTC/out.wav", "started_at": "2024-03-03T01:09:42.024690Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vgvrgobbfp77oizo6x523fpvsi", "cancel": "https://api.replicate.com/v1/predictions/vgvrgobbfp77oizo6x523fpvsi/cancel" }, "version": "2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f" }Generated in> Text split to sentences. テキスト読み上げの分野は最近急速な発展を遂げています > =========================== 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:03<00:00, 3.28s/it] 100%|██████████| 1/1 [00:03<00:00, 3.28s/it]Prediction
cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688IDjmssl4zb5vsqvdjdcfpkxzjzwiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- The field of text-to-speech has seen rapid development recently.
- speed
- 1
- speaker
- EN-BR
- language
- EN
{ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", { input: { text: "The field of text-to-speech has seen rapid development recently.", speed: 1, speaker: "EN-BR", language: "EN" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", input={ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-03T01:13:33.471124Z", "created_at": "2024-03-03T01:10:34.653158Z", "data_removed": false, "error": null, "id": "jmssl4zb5vsqvdjdcfpkxzjzwi", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }, "logs": "> Text split to sentences.\nThe field of text-to-speech has seen rapid development recently.\n> ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\nmodel.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]\u001b[A\nmodel.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 96.0MB/s]\u001b[A\nmodel.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 143MB/s] \u001b[A\nmodel.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 159MB/s]\u001b[A\nmodel.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 167MB/s]\u001b[A\nmodel.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 172MB/s]\u001b[A\nmodel.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 178MB/s] \u001b[A\nmodel.safetensors: 31%|███ | 136M/440M [00:00<00:01, 180MB/s]\u001b[A\nmodel.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 182MB/s]\u001b[A\nmodel.safetensors: 40%|████ | 178M/440M [00:01<00:01, 183MB/s]\u001b[A\nmodel.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s]\u001b[A\nmodel.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s]\u001b[A\nmodel.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 183MB/s]\u001b[A\nmodel.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 185MB/s]\u001b[A\nmodel.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 184MB/s]\u001b[A\nmodel.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 182MB/s]\u001b[A\nmodel.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 183MB/s]\u001b[A\nmodel.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 185MB/s]\u001b[A\nmodel.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 186MB/s]\u001b[A\nmodel.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 178MB/s]\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n100%|██████████| 1/1 [00:11<00:00, 11.60s/it]\n100%|██████████| 1/1 [00:11<00:00, 11.60s/it]", "metrics": { "predict_time": 11.988441, "total_time": 178.817966 }, "output": "https://replicate.delivery/pbxt/vOmNvhkctwLuM1fPwYlW89zEZKEcu2IJR75dz0FwMQ3eCYcSA/out.wav", "started_at": "2024-03-03T01:13:21.482683Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jmssl4zb5vsqvdjdcfpkxzjzwi", "cancel": "https://api.replicate.com/v1/predictions/jmssl4zb5vsqvdjdcfpkxzjzwi/cancel" }, "version": "ac8dc5ff8d04a44bb43dfce7599dd128bcbf81f3e9f6faee74f8a6d6f2bfb688" }Generated in> Text split to sentences. The field of text-to-speech has seen rapid development recently. > =========================== 0%| | 0/1 [00:00<?, ?it/s] model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s] model.safetensors: 2%|▏ | 10.5M/440M [00:00<00:04, 96.0MB/s] model.safetensors: 7%|▋ | 31.5M/440M [00:00<00:02, 143MB/s] model.safetensors: 12%|█▏ | 52.4M/440M [00:00<00:02, 159MB/s] model.safetensors: 17%|█▋ | 73.4M/440M [00:00<00:02, 167MB/s] model.safetensors: 21%|██▏ | 94.4M/440M [00:00<00:02, 172MB/s] model.safetensors: 26%|██▌ | 115M/440M [00:00<00:01, 178MB/s] model.safetensors: 31%|███ | 136M/440M [00:00<00:01, 180MB/s] model.safetensors: 36%|███▌ | 157M/440M [00:00<00:01, 182MB/s] model.safetensors: 40%|████ | 178M/440M [00:01<00:01, 183MB/s] model.safetensors: 45%|████▌ | 199M/440M [00:01<00:01, 182MB/s] model.safetensors: 50%|████▉ | 220M/440M [00:01<00:01, 183MB/s] model.safetensors: 55%|█████▍ | 241M/440M [00:01<00:01, 183MB/s] model.safetensors: 60%|█████▉ | 262M/440M [00:01<00:00, 184MB/s] model.safetensors: 64%|██████▍ | 283M/440M [00:01<00:00, 184MB/s] model.safetensors: 69%|██████▉ | 304M/440M [00:01<00:00, 185MB/s] model.safetensors: 74%|███████▍ | 325M/440M [00:01<00:00, 184MB/s] model.safetensors: 79%|███████▊ | 346M/440M [00:01<00:00, 184MB/s] model.safetensors: 83%|████████▎ | 367M/440M [00:02<00:00, 182MB/s] model.safetensors: 88%|████████▊ | 388M/440M [00:02<00:00, 183MB/s] model.safetensors: 93%|█████████▎| 409M/440M [00:02<00:00, 185MB/s] model.safetensors: 98%|█████████▊| 430M/440M [00:02<00:00, 186MB/s] model.safetensors: 100%|██████████| 440M/440M [00:02<00:00, 178MB/s] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 100%|██████████| 1/1 [00:11<00:00, 11.60s/it] 100%|██████████| 1/1 [00:11<00:00, 11.60s/it]Prediction
cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6fID5pvjbhbbjg743mpy47pwtqygpuStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- text
- The field of text-to-speech has seen rapid development recently.
- speed
- 1
- speaker
- EN-BR
- language
- EN
{ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", { input: { text: "The field of text-to-speech has seen rapid development recently.", speed: 1, speaker: "EN-BR", language: "EN" } } ); // 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 replicateImport the client:import replicateRun cjwbw/melotts using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", input={ "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cjwbw/melotts 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": "cjwbw/melotts:2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x- 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": "2024-03-03T01:20:12.902255Z", "created_at": "2024-03-03T01:20:10.005501Z", "data_removed": false, "error": null, "id": "5pvjbhbbjg743mpy47pwtqygpu", "input": { "text": "The field of text-to-speech has seen rapid development recently.", "speed": 1, "speaker": "EN-BR", "language": "EN" }, "logs": "> Text split to sentences.\nThe field of text-to-speech has seen rapid development recently.\n> ===========================\n 0%| | 0/1 [00:00<?, ?it/s]\nmodel.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]\u001b[A\nmodel.safetensors: 0%| | 47.8k/440M [00:00<19:35, 375kB/s]\u001b[A\nmodel.safetensors: 2%|▏ | 10.5M/440M [00:00<00:13, 32.6MB/s]\u001b[A\nmodel.safetensors: 7%|▋ | 31.5M/440M [00:00<00:05, 81.1MB/s]\u001b[A\nmodel.safetensors: 19%|█▉ | 83.9M/440M [00:00<00:01, 199MB/s] \u001b[A\nmodel.safetensors: 33%|███▎ | 147M/440M [00:00<00:00, 312MB/s] \u001b[A\nmodel.safetensors: 69%|██████▉ | 304M/440M [00:00<00:00, 659MB/s]\u001b[A\nmodel.safetensors: 100%|█████████▉| 440M/440M [00:00<00:00, 462MB/s]\nSome weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n100%|██████████| 1/1 [00:02<00:00, 2.49s/it]\n100%|██████████| 1/1 [00:02<00:00, 2.49s/it]", "metrics": { "predict_time": 2.880707, "total_time": 2.896754 }, "output": "https://replicate.delivery/pbxt/asUAOj69JLJpO5t837vUh4NVC4CBt9BZR1DbeTXtn4JmEMOJA/out.wav", "started_at": "2024-03-03T01:20:10.021548Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5pvjbhbbjg743mpy47pwtqygpu", "cancel": "https://api.replicate.com/v1/predictions/5pvjbhbbjg743mpy47pwtqygpu/cancel" }, "version": "2e4d356f3715d98c183ef097ce2cf410def83ca9fbbdd5f8a32ba056123e6a6f" }Generated in> Text split to sentences. The field of text-to-speech has seen rapid development recently. > =========================== 0%| | 0/1 [00:00<?, ?it/s] model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s] model.safetensors: 0%| | 47.8k/440M [00:00<19:35, 375kB/s] model.safetensors: 2%|▏ | 10.5M/440M [00:00<00:13, 32.6MB/s] model.safetensors: 7%|▋ | 31.5M/440M [00:00<00:05, 81.1MB/s] model.safetensors: 19%|█▉ | 83.9M/440M [00:00<00:01, 199MB/s] model.safetensors: 33%|███▎ | 147M/440M [00:00<00:00, 312MB/s] model.safetensors: 69%|██████▉ | 304M/440M [00:00<00:00, 659MB/s] model.safetensors: 100%|█████████▉| 440M/440M [00:00<00:00, 462MB/s] Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight'] - This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 100%|██████████| 1/1 [00:02<00:00, 2.49s/it] 100%|██████████| 1/1 [00:02<00:00, 2.49s/it]
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