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app.py

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+from utils_violin_transcript import PretrainedModel
+from json import load as json_load
+from huggingface_hub import hf_hub_download
+from torch import device as Device
+from torch.cuda import is_available as cuda_is_available
+from transformers import Pop2PianoForConditionalGeneration, Pop2PianoProcessor
+from librosa import load as librosa_load, piptrack, hz_to_midi
+from mido import MidiFile, MidiTrack, Message, MetaMessage, bpm2tempo
+from basic_pitch.inference import predict as basic_pitch_predict
+from numpy import argmax as np_argmax, isnan as np_isnan
+
+device = Device("cuda" if cuda_is_available() else "cpu")
+
+class Pop2Piano:
+    def __init__(self,device:Device,model_id_path="sweetcocoa/pop2piano"):
+        self.model = Pop2PianoForConditionalGeneration.from_pretrained(model_id_path).to(device)
+        self.processor = Pop2PianoProcessor.from_pretrained(model_id_path)
+    def audio2midi(self,input,composer,num_bars,num_beams,steps_per_beat):
+        data, sr = librosa_load(input, sr=None)
+        inputs = self.processor(data, sr, steps_per_beat,return_tensors="pt",num_bars=num_bars)
+        self.processor.batch_decode(self.model.generate(num_beams=num_beams,do_sample=True,input_features=inputs["input_features"], composer="composer" + str(composer)),inputs)["pretty_midi_objects"][0].write(open("output.mid", "wb"))
+        return "output.mid"
+
+def smooth_pitch_sequence(pitches, magnitudes, threshold=0.1):
+    midi_sequence = []
+    for i in range(pitches.shape[1]):
+        index = np_argmax(magnitudes[:, i])
+        pitch_mag = magnitudes[index, i]
+        pitch = pitches[index, i]
+        if pitch_mag < threshold or np_isnan(pitch) or pitch <= 0:
+            midi_sequence.append(None)
+        else:
+            midi_note = int(round(hz_to_midi(pitch)))
+            midi_sequence.append(midi_note)
+    return midi_sequence
+
+def clean_midi_sequence(sequence, min_note_length=2):
+    cleaned = []
+    current_note = None
+    count = 0
+    for note in sequence + [None]:
+        if note == current_note:
+            count += 1
+        else:
+            if current_note is not None and count >= min_note_length:
+                cleaned.extend([current_note] * count)
+            else:
+                cleaned.extend([None] * count)
+            current_note = note
+            count = 1
+    return cleaned
+
+def basic_to_midi(input_file, tempo_bpm=120):
+    wav, sr = librosa_load(input_file)
+    audio_duration = len(wav) / sr
+    pitches, magnitudes = piptrack(y=wav, sr=sr, hop_length=512)
+    midi_sequence = clean_midi_sequence(smooth_pitch_sequence(pitches, magnitudes))
+    total_frames = len(midi_sequence)
+    ticks_per_beat = 480
+    tempo = bpm2tempo(tempo_bpm)
+    ticks_per_second = (ticks_per_beat * tempo_bpm) / 60
+    time_per_frame = max(1, round((audio_duration * ticks_per_second) / total_frames))
+    midi_file = MidiFile(ticks_per_beat=ticks_per_beat)
+    track = MidiTrack()
+    midi_file.tracks.append(track)
+    track.append(MetaMessage('set_tempo', tempo=tempo))
+    last_note = None
+    duration = 0
+    for note in midi_sequence:
+        if note != last_note:
+            if last_note is not None:
+                track.append(Message('note_off', note=last_note, velocity=0, time=duration))
+                duration = 0
+            if note is not None:
+                track.append(Message('note_on', note=note, velocity=100, time=0))
+            last_note = note
+        duration += time_per_frame
+    if last_note is not None:
+        track.append(Message('note_off', note=last_note, velocity=0, time=duration))
+    midi_file.save("output.mid")
+    return "output.mid"
+
+def spotify_to_midi(input_audio_path,tempo=120):
+    _, midi_data, _ = basic_pitch_predict(input_audio_path,midi_tempo=tempo)
+    midi_data.write("output.mid")
+    mid = MidiFile("output.mid")
+    for i, track in enumerate(mid.tracks):
+        mid.tracks[i] = [msg for msg in track if msg.type != 'program_change']
+    mid.save("output.mid")
+    return "output.mid"
+
+
+import gradio as gr
+gr.TabbedInterface([gr.Interface(basic_to_midi,[gr.Audio(type="filepath",label="Input Audio"),gr.Number(120,label="BPM")],gr.File(label="Midi File")),gr.Interface(spotify_to_midi,[gr.Audio(type="filepath",label="Input Audio"),gr.Number(120,label="BPM")],gr.File(label="Midi File")),gr.Interface(PretrainedModel(json_load(open(hf_hub_download("shethjenil/Audio2ViolinMidi","violin.json"))),hf_hub_download("shethjenil/Audio2ViolinMidi","violin_model.pt"),device).transcribe_music, [gr.Audio(label="Upload your Audio file",type="filepath"),gr.Number(32,label="Batch size"),gr.Radio(["spotify","rebab","tiktok"],value="spotify",label="Post Processing")],gr.File(label="Download MIDI file")),gr.Interface(Pop2Piano(device).audio2midi,[gr.Audio(label="Input Audio",type="filepath"),gr.Number(1, minimum=1, maximum=21, label="Composer"),gr.Number(2,label="Details in Piano"),gr.Number(1,label="Efficiency of Piano"),gr.Radio([1,2,4],label="steps per beat",value=2)],gr.File(label="MIDI File"))],["Basic","Medium","Advance","More Advance"]).launch()
+

requirements.txt

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+numpy
+scipy
+numba
+matplotlib
+librosa
+mido
+torchaudio
+mir_eval
+pretty_midi @ git+https://github.com/craffel/pretty-midi.git
+basic-pitch[coreml,tf,onnx]
+torch
+huggingface-hub
+essentia
+resampy
+transformers==4.45.2