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Update README.md with HF implementation (#5)

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- Update README.md with HF implementation (a05d7f56e82115620dbc41fcec5a0a2d8c8715dc)
- retain audiocraft usage (7235210de72083cf126290e8d20d18a146ffa95e)
- fix typos (dc41f5125ba7117f660d6dc438b680a2e488c934)

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  1. README.md +70 -12
README.md CHANGED
@@ -6,9 +6,9 @@ license: cc-by-nc-4.0
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  # MusicGen - Small - 300M
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- Audiocraft provides the code and models for MusicGen, a simple and controllable model for music generation.
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- MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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- Unlike existing methods like MusicLM, MusicGen doesn't not require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
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  By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
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  MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*.
@@ -23,15 +23,75 @@ Four checkpoints are released:
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  Try out MusicGen yourself!
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- - <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
 
 
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  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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  </a>
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- - <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
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- <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
 
 
 
 
 
 
 
 
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  </a>
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- - You can run MusicGen locally as well:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
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  ```
@@ -46,17 +106,15 @@ apt get install ffmpeg
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  3. Run the following Python code:
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  ```py
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- import torchaudio
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-
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  from audiocraft.models import MusicGen
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  from audiocraft.data.audio import audio_write
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- model = MusicGen.get_pretrained('small')
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  model.set_generation_params(duration=8) # generate 8 seconds.
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- descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
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- wav = model.generate(descriptions) # generates 3 samples.
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  for idx, one_wav in enumerate(wav):
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  # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
 
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  # MusicGen - Small - 300M
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+ MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
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+ It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
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+ Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
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  By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
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  MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*.
 
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  Try out MusicGen yourself!
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+ * Audiocraft Colab:
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+
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+ <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
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  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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  </a>
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+ * Hugging Face Colab:
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+
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+ <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb">
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+ <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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+ </a>
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+
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+ * Hugging Face Demo:
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+
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+ <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
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+ <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
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  </a>
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+ ## 🤗 Transformers Usage
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+
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+ You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
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+
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+ 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main:
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+
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+ ```
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+ pip install git+https://github.com/huggingface/transformers.git
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+ ```
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+
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+ 2. Run the following Python code to generate text-conditional audio samples:
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+
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+ ```py
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+ from transformers import AutoProcessor, MusicgenForConditionalGeneration
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+
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+
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+ processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
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+ model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
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+
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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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+
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+ audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)
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+ ```
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+
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+ 3. Listen to the audio samples either in an ipynb notebook:
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+
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+ ```py
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+ from IPython.display import Audio
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+
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+ sampling_rate = model.config.audio_encoder.sampling_rate
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+ Audio(audio_values[0].numpy(), rate=sampling_rate)
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+ ```
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+
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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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+ ```py
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+ import scipy
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+
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+ sampling_rate = model.config.audio_encoder.sampling_rate
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+ scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy())
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+ ```
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+
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+ For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen).
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+
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+ ## Audiocraft Usage
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+
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+ You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
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  1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
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  ```
 
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  3. Run the following Python code:
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  ```py
 
 
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  from audiocraft.models import MusicGen
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  from audiocraft.data.audio import audio_write
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+ model = MusicGen.get_pretrained("small")
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  model.set_generation_params(duration=8) # generate 8 seconds.
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+ descriptions = ["happy rock", "energetic EDM"]
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+ wav = model.generate(descriptions) # generates 2 samples.
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  for idx, one_wav in enumerate(wav):
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  # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.