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declare-lab
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tango

Text-to-Audio
Transformers
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Inference Endpoints
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Update README.md

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  1. README.md +2 -2
README.md CHANGED
@@ -27,7 +27,7 @@ audio = tango.generate(prompt)
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  sf.write(f"{prompt}.wav", audio, samplerate=16000)
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  IPython.display.Audio(data=audio, rate=16000)
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  ```
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- [CheerClap.webm](https://user-images.githubusercontent.com/13917097/233851915-e702524d-cd35-43f7-93e0-86ea579231a7.webm)
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  The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.
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@@ -38,7 +38,7 @@ prompt = "Rolling thunder with lightning strikes"
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  audio = tango.generate(prompt, steps=200)
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  IPython.display.Audio(data=audio, rate=16000)
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  ```
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- [Thunder.webm](https://user-images.githubusercontent.com/13917097/233851929-90501e41-911d-453f-a00b-b215743365b4.webm)
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  <!-- [MachineClicking](https://user-images.githubusercontent.com/25340239/233857834-bfda52b4-4fcc-48de-b47a-6a6ddcb3671b.mp4 "sample 1") -->
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  sf.write(f"{prompt}.wav", audio, samplerate=16000)
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  IPython.display.Audio(data=audio, rate=16000)
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  ```
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+ [An audience cheering and clapping.webm](https://user-images.githubusercontent.com/13917097/233851915-e702524d-cd35-43f7-93e0-86ea579231a7.webm)
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  The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.
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  audio = tango.generate(prompt, steps=200)
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  IPython.display.Audio(data=audio, rate=16000)
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  ```
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+ [Rolling thunder with lightning strikes.webm](https://user-images.githubusercontent.com/13917097/233851929-90501e41-911d-453f-a00b-b215743365b4.webm)
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  <!-- [MachineClicking](https://user-images.githubusercontent.com/25340239/233857834-bfda52b4-4fcc-48de-b47a-6a6ddcb3671b.mp4 "sample 1") -->
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