Update README.md

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  1. README.md +7 -6
README.md CHANGED
@@ -55,22 +55,23 @@ Try out MusicGen yourself!
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  ## πŸ€— Transformers Usage
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- You can run MusicGen locally with the πŸ€— Transformers library from version 4.31.0 onwards.
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  1. First install the πŸ€— [Transformers library](https://github.com/huggingface/transformers) and scipy:
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  ```
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  pip install --upgrade pip
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- pip install --upgrade transformers scipy
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  ```
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  2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!
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  ```python
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- from transformers import pipeline
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  import scipy
 
 
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- synthesiser = pipeline("text-to-audio", "facebook/musicgen-stereo-large")
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  music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True})
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@@ -83,13 +84,13 @@ scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], music=au
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  from transformers import AutoProcessor, MusicgenForConditionalGeneration
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  processor = AutoProcessor.from_pretrained("facebook/musicgen-stereo-large")
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- model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-stereo-large")
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  inputs = processor(
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  text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
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  padding=True,
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  return_tensors="pt",
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- )
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  audio_values = model.generate(**inputs, max_new_tokens=256)
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  ```
 
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  ## πŸ€— Transformers Usage
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+ You can run MusicGen Stereo models locally with the πŸ€— Transformers library from `main` onward.
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  1. First install the πŸ€— [Transformers library](https://github.com/huggingface/transformers) and scipy:
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  ```
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  pip install --upgrade pip
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+ pip install --upgrade git+https://github.com/huggingface/transformers.git scipy
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  ```
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  2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!
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  ```python
 
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  import scipy
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+ import torch
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+ from transformers import pipeline
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+ synthesiser = pipeline("text-to-audio", "facebook/musicgen-stereo-large", torch_dtype=torch.float16, device="cuda")
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  music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True})
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  from transformers import AutoProcessor, MusicgenForConditionalGeneration
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  processor = AutoProcessor.from_pretrained("facebook/musicgen-stereo-large")
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+ model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-stereo-large").to("cuda")
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  inputs = processor(
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  text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
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  padding=True,
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  return_tensors="pt",
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+ ).to("cuda")
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  audio_values = model.generate(**inputs, max_new_tokens=256)
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  ```