Create app.py

app.py

@@ -0,0 +1,132 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("TristanBehrens/js-fakes-4bars")
+model = AutoModelForCausalLM.from_pretrained("TristanBehrens/js-fakes-4bars")
+
+import note_seq
+
+NOTE_LENGTH_16TH_120BPM = 0.25 * 60 / 120
+BAR_LENGTH_120BPM = 4.0 * 60 / 120
+SAMPLE_RATE=44100
+
+def token_sequence_to_note_sequence(token_sequence, use_program=True, use_drums=True, instrument_mapper=None, only_piano=False):
+    if isinstance(token_sequence, str):
+        token_sequence = token_sequence.split()
+    note_sequence = empty_note_sequence()
+
+    # Render all notes.
+    current_program = 1
+    current_is_drum = False
+    current_instrument = 0
+    track_count = 0
+    for token_index, token in enumerate(token_sequence):
+
+        if token == "PIECE_START":
+            pass
+        elif token == "PIECE_END":
+            print("The end.")
+            break
+        elif token == "TRACK_START":
+            current_bar_index = 0
+            track_count += 1
+            pass
+        elif token == "TRACK_END":
+            pass
+        elif token == "KEYS_START":
+            pass
+        elif token == "KEYS_END":
+            pass
+        elif token.startswith("KEY="):
+            pass
+        elif token.startswith("INST"):
+            instrument = token.split("=")[-1]
+            if instrument != "DRUMS" and use_program:
+                if instrument_mapper is not None:
+                    if instrument in instrument_mapper:
+                        instrument = instrument_mapper[instrument]
+                current_program = int(instrument)
+                current_instrument = track_count
+                current_is_drum = False
+            if instrument == "DRUMS" and use_drums:
+                current_instrument = 0
+                current_program = 0
+                current_is_drum = True
+        elif token == "BAR_START":
+            current_time = current_bar_index * BAR_LENGTH_120BPM
+            current_notes = {}
+        elif token == "BAR_END":
+            current_bar_index += 1
+            pass
+        elif token.startswith("NOTE_ON"):
+            pitch = int(token.split("=")[-1])
+            note = note_sequence.notes.add()
+            note.start_time = current_time
+            note.end_time = current_time + 4 * NOTE_LENGTH_16TH_120BPM
+            note.pitch = pitch
+            note.instrument = current_instrument
+            note.program = current_program
+            note.velocity = 80
+            note.is_drum = current_is_drum
+            current_notes[pitch] = note
+        elif token.startswith("NOTE_OFF"):
+            pitch = int(token.split("=")[-1])
+            if pitch in current_notes:
+                note = current_notes[pitch]
+                note.end_time = current_time
+        elif token.startswith("TIME_DELTA"):
+            delta = float(token.split("=")[-1]) * NOTE_LENGTH_16TH_120BPM
+            current_time += delta
+        elif token.startswith("DENSITY="):
+            pass
+        elif token == "[PAD]":
+            pass
+        else:
+            #print(f"Ignored token {token}.")
+            pass
+
+    # Make the instruments right.
+    instruments_drums = []
+    for note in note_sequence.notes:
+        pair = [note.program, note.is_drum]
+        if pair not in instruments_drums:
+            instruments_drums += [pair]
+        note.instrument = instruments_drums.index(pair)
+
+    if only_piano:
+        for note in note_sequence.notes:
+            if not note.is_drum:
+                note.instrument = 0
+                note.program = 0
+
+    return note_sequence
+
+def empty_note_sequence(qpm=120.0, total_time=0.0):
+    note_sequence = note_seq.protobuf.music_pb2.NoteSequence()
+    note_sequence.tempos.add().qpm = qpm
+    note_sequence.ticks_per_quarter = note_seq.constants.STANDARD_PPQ
+    note_sequence.total_time = total_time
+    return note_sequence
+
+def process(text):
+  input_ids = tokenizer.encode(text, return_tensors="pt")
+  generated_ids = model.generate(input_ids, max_length=500)
+  generated_sequence = tokenizer.decode(generated_ids[0])
+  print(generated_sequence)
+
+  # Convert text of notes to audio
+  note_sequence = token_sequence_to_note_sequence(generated_sequence)
+  synth = note_seq.midi_synth.synthesize
+  array_of_floats = synth(note_sequence, sample_rate=SAMPLE_RATE)
+  return SAMPLE_RATE, array_of_floats
+  
+title = "Music generation with GPT-2"
+
+iface = gr.Interface(
+  fn=process, 
+  inputs=[gr.inputs.Textbox(placeholder="PIECE_START")],
+  outputs='audio',
+  title=title,
+  examples=[["PIECE_START"], ["PIECE_START STYLE=JSFAKES GENRE=JSFAKES TRACK_START INST=48 BAR_START NOTE_ON=61"]]
+)
+
+iface.launch(debug=True)