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README.md
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@@ -46,17 +46,30 @@ Try out MusicGen yourself!
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You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
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1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers)
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```
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pip install
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```
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2. Run the
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```
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from transformers import
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processor = AutoProcessor.from_pretrained("facebook/musicgen-large")
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model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large")
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audio_values = model.generate(**inputs, max_new_tokens=256)
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```
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```
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from IPython.display import Audio
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sampling_rate = model.config.audio_encoder.sampling_rate
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Or save them as a `.wav` file using a third-party library, e.g. `scipy`:
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```
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import scipy
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sampling_rate = model.config.audio_encoder.sampling_rate
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You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
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1. First install the 🤗 [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-large")
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music = pipe("lo-fi music with a soothing melody", forward_params={"do_sample": True})
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scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], music=audio["audio"])
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```
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3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control.
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```python
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from transformers import AutoProcessor, MusicgenForConditionalGeneration
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processor = AutoProcessor.from_pretrained("facebook/musicgen-large")
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model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large")
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audio_values = model.generate(**inputs, max_new_tokens=256)
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```
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4. Listen to the audio samples either in an ipynb notebook:
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```python
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from IPython.display import Audio
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sampling_rate = model.config.audio_encoder.sampling_rate
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Or save them as a `.wav` file using a third-party library, e.g. `scipy`:
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```python
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import scipy
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sampling_rate = model.config.audio_encoder.sampling_rate
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