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| import numpy as np | |
| import tensorflow as tf | |
| import librosa | |
| git clone https://huggingface.co/amongusrickroll68/MeloMind | |
| from diffusers import DiffusionPipeline | |
| pipeline = DiffusionPipeline.from_pretrained("amongusrickroll68/MeloMind") | |
| class TextToMusicGenerator: | |
| def __init__(self): | |
| self.model = tf.keras.models.load_model('path/to/model') | |
| self.sampling_rate = 22050 | |
| def generate_music(self, prompt): | |
| prompt_encoded = self._encode_prompt(prompt) | |
| sequence = self._generate_sequence(prompt_encoded) | |
| audio = self._sequence_to_audio(sequence) | |
| return audio | |
| def _encode_prompt(self, prompt): | |
| # encode text prompt as input for the model | |
| # ... | |
| return prompt_encoded | |
| def _generate_sequence(self, prompt_encoded): | |
| # generate sequence of musical notes from encoded prompt | |
| # ... | |
| return sequence | |
| def _sequence_to_audio(self, sequence): | |
| # convert sequence to audio waveform | |
| notes = self._sequence_to_notes(sequence) | |
| audio = self._notes_to_audio(notes) | |
| return audio | |
| def _sequence_to_notes(self, sequence): | |
| # convert sequence of musical notes to Note objects | |
| # ... | |
| return notes | |
| def _notes_to_audio(self, notes): | |
| # convert Note objects to audio waveform | |
| # ... | |
| return audio | |
| generator = TextToMusicGenerator() | |
| prompt = "Generate a cheerful and upbeat song in the key of C major with a tempo of 120 bpm" | |
| audio = generator.generate_music(prompt) | |
| librosa.output.write_wav('generated_music.wav', audio, sr=generator.sampling_rate) | |