Fix training and inference

.gitignore

@@ -1,6 +1,5 @@
 # data files
 *.csv
-*.json
 *.xls
 *.xlsx
 *.pkl

amt/audio.py

@@ -191,15 +191,15 @@ def __init__(
         max_snr: int = 50,
         max_dist_gain: int = 25,
         min_dist_gain: int = 0,
-        noise_ratio: float = 0.95,
-        reverb_ratio: float = 0.95,
+        noise_ratio: float = 0.75,
+        reverb_ratio: float = 0.75,
         applause_ratio: float = 0.01,
         bandpass_ratio: float = 0.15,
         distort_ratio: float = 0.15,
         reduce_ratio: float = 0.01,
-        detune_ratio: float = 0.1,
-        detune_max_shift: float = 0.15,
-        spec_aug_ratio: float = 0.5,
+        detune_ratio: float = 0.0,
+        detune_max_shift: float = 0.0,
+        spec_aug_ratio: float = 0.9,
     ):
         super().__init__()
         self.tokenizer = AmtTokenizer()
@@ -223,7 +223,10 @@ def __init__(
         self.detune_ratio = detune_ratio
         self.detune_max_shift = detune_max_shift
         self.spec_aug_ratio = spec_aug_ratio
-        self.reduction_resample_rate = 6000  # Hardcoded?
+
+        self.time_mask_param = 2500
+        self.freq_mask_param = 15
+        self.reduction_resample_rate = 6000
 
         # Audio aug
         impulse_paths = self._get_paths(
@@ -263,10 +266,10 @@ def __init__(
         )
         self.spec_aug = torch.nn.Sequential(
             torchaudio.transforms.FrequencyMasking(
-                freq_mask_param=15, iid_masks=True
+                freq_mask_param=self.freq_mask_param, iid_masks=True
             ),
             torchaudio.transforms.TimeMasking(
-                time_mask_param=1000, iid_masks=True
+                time_mask_param=self.time_mask_param, iid_masks=True
             ),
         )
 
@@ -281,6 +284,8 @@ def get_params(self):
             "detune_ratio": self.detune_ratio,
             "detune_max_shift": self.detune_max_shift,
             "spec_aug_ratio": self.spec_aug_ratio,
+            "time_mask_param": self.time_mask_param,
+            "freq_mask_param": self.freq_mask_param,
         }
 
     def _get_paths(self, dir_path):

amt/data.py

@@ -1,6 +1,8 @@
 import mmap
 import os
 import io
+import random
+import shlex
 import base64
 import shutil
 import orjson
@@ -16,14 +18,22 @@
 from amt.audio import pad_or_trim
 
 
-# Occasionally the worker util goes to 0 for some reason, debug this
+def _check_onset_threshold(seq: list, onset: int):
+    for tok_1, tok_2 in zip(seq, seq[1:]):
+        if isinstance(tok_1, tuple) and tok_1[0] in ("on", "off"):
+            _onset = tok_2[1]
+            if _onset > onset:
+                return True
+
+    return False
 
 
 def get_wav_mid_segments(
     audio_path: str,
     mid_path: str = "",
     return_json: bool = False,
     stride_factor: int | None = None,
+    pad_last=False,
 ):
     """This function yields tuples of matched log mel spectrograms and
     tokenized sequences (np.array, list). If it is given only an audio path
@@ -61,10 +71,12 @@ def get_wav_mid_segments(
 
     # Create features
     total_samples = wav.shape[-1]
+    pad_factor = 2 if pad_last is True else 1
     res = []
     for idx in range(
         0,
-        total_samples - (num_samples - num_samples // stride_factor),
+        total_samples
+        - (num_samples - pad_factor * (num_samples // stride_factor)),
         num_samples // stride_factor,
     ):
         audio_feature = pad_or_trim(wav[idx:], length=num_samples)
@@ -75,6 +87,12 @@ def get_wav_mid_segments(
                 end_ms=(idx + num_samples) / samples_per_ms,
                 max_pedal_len_ms=10000,
             )
+
+            # Hardcoded to 2.5s
+            if _check_onset_threshold(mid_feature, 2500) is False:
+                print("No note messages after 2.5s - skipping")
+                continue
+
         else:
             mid_feature = []
 
@@ -86,6 +104,56 @@ def get_wav_mid_segments(
     return res
 
 
+def pianoteq_cmd_fn(mid_path: str, wav_path: str):
+    presets = [
+        "C. Bechstein",
+        "C. Bechstein Close Mic",
+        "C. Bechstein Under Lid",
+        "C. Bechstein 440",
+        "C. Bechstein Recording",
+        "C. Bechstein Werckmeister III",
+        "C. Bechstein Neidhardt III",
+        "C. Bechstein mesotonic",
+        "C. Bechstein well tempered",
+        "HB Steinway D Blues",
+        "HB Steinway D Pop",
+        "HB Steinway D New Age",
+        "HB Steinway D Prelude",
+        "HB Steinway D Felt I",
+        "HB Steinway D Felt II",
+        "HB Steinway Model D",
+        "HB Steinway D Classical Recording",
+        "HB Steinway D Jazz Recording",
+        "HB Steinway D Chamber Recording",
+        "HB Steinway D Studio Recording",
+        "HB Steinway D Intimate",
+        "HB Steinway D Cinematic",
+        "HB Steinway D Close Mic Classical",
+        "HB Steinway D Close Mic Jazz",
+        "HB Steinway D Player Wide",
+        "HB Steinway D Player Clean",
+        "HB Steinway D Trio",
+        "HB Steinway D Duo",
+        "HB Steinway D Cabaret",
+        "HB Steinway D Bright",
+        "HB Steinway D Hyper Bright",
+        "HB Steinway D Prepared",
+        "HB Steinway D Honky Tonk",
+    ]
+
+    preset = random.choice(presets)
+
+    # Safely quote the preset name, MIDI path, and WAV path
+    safe_preset = shlex.quote(preset)
+    safe_mid_path = shlex.quote(mid_path)
+    safe_wav_path = shlex.quote(wav_path)
+
+    # Construct the command
+    command = f"/home/mchorse/pianoteq/x86-64bit/Pianoteq\\ 8\\ STAGE --preset {safe_preset} --midi {safe_mid_path} --wav {safe_wav_path}"
+
+    return command
+
+
 def write_features(audio_path: str, mid_path: str, save_path: str):
     features = get_wav_mid_segments(
         audio_path=audio_path,
@@ -121,7 +189,7 @@ def write_synth_features(cli_cmd_fn: Callable, mid_path: str, save_path: str):
 
     try:
         get_synth_audio(
-            cli_cmd=cli_cmd_fn, mid_path=mid_path, wav_path=audio_path_temp
+            cli_cmd_fn=cli_cmd_fn, mid_path=mid_path, wav_path=audio_path_temp
         )
     except:
         if os.path.isfile(audio_path_temp):
@@ -133,7 +201,11 @@ def write_synth_features(cli_cmd_fn: Callable, mid_path: str, save_path: str):
             mid_path=mid_path,
             return_json=False,
         )
-        os.remove(audio_path_temp)
+
+        if os.path.isfile(audio_path_temp):
+            os.remove(audio_path_temp)
+
+    print(f"Found {len(features)}")
 
     with open(save_path, mode="a") as file:
         for wav, seq in features:
@@ -174,7 +246,11 @@ def build_synth_worker_fn(
 
     while not load_path_queue.empty():
         mid_path = load_path_queue.get()
-        write_synth_features(cli_cmd, mid_path, worker_save_path)
+        try:
+            write_synth_features(cli_cmd, mid_path, worker_save_path)
+        except Exception as e:
+            print("Failed")
+            print(e)
 
     save_path_queue.put(worker_save_path)
 
@@ -239,7 +315,7 @@ def _format(tok):
             seq_len=self.config["max_seq_len"],
         )
 
-        return wav, self.tokenizer.encode(src), self.tokenizer.encode(tgt)
+        return wav, self.tokenizer.encode(src), self.tokenizer.encode(tgt), idx
 
     def _build_index(self):
         self.file_mmap.seek(0)
@@ -254,7 +330,7 @@ def _build_index(self):
 
         return index
 
-    def _save_index(self, index: list[int], save_path: str):
+    def _save_index(self, index: list, save_path: str):
         with open(save_path, "w") as file:
             for idx in index:
                 file.write(f"{idx}\n")
@@ -325,7 +401,7 @@ def build(
             ]
         else:
             # Build synthetic dataset
-            assert len(load_paths[0]) == 1, "Invalid load paths"
+            assert isinstance(load_paths[0], str), "Invalid load paths"
             print("Building synthetic dataset")
             worker_processes = [
                 Process(

amt/evaluate.py

@@ -42,27 +42,32 @@ def midi_to_hz(note, shift=0):
     # return (a / 32) * (2 ** ((note - 9) / 12))
 
 
+def get_matched_files(est_dir: str, ref_dir: str):
+    # We assume that the files have the same path relative to their directory
+
+    res = []
+    est_paths = glob.glob(os.path.join(est_dir, "**/*.mid"), recursive=True)
+    print(f"found {len(est_paths)} est files")
+
+    for est_path in est_paths:
+        est_rel_path = os.path.relpath(est_path, est_dir)
+        ref_path = os.path.join(
+            ref_dir, os.path.splitext(est_rel_path)[0] + ".midi"
+        )
+        if os.path.isfile(ref_path):
+            res.append((est_path, ref_path))
+
+    print(f"found {len(res)} matched est-ref pairs")
+
+    return res
+
+
 def evaluate_mir_eval(est_dir, ref_dir, output_stats_file=None, est_shift=0):
     """
     Evaluate the estimated pitches against the reference pitches using mir_eval.
     """
-    # Evaluate the estimated pitches against the reference pitches
-    ref_midi_files = glob.glob(f"{ref_dir}/*.mid*")
-    est_midi_files = glob.glob(f"{est_dir}/*.mid*")
-
-    est_ref_pairs = []
-    for est_fpath in est_midi_files:
-        ref_fpath = os.path.join(ref_dir, os.path.basename(est_fpath))
-        if ref_fpath in ref_midi_files:
-            est_ref_pairs.append((est_fpath, ref_fpath))
-        if ref_fpath.replace(".mid", ".midi") in ref_midi_files:
-            est_ref_pairs.append(
-                (est_fpath, ref_fpath.replace(".mid", ".midi"))
-            )
-        else:
-            print(
-                f"Reference file not found for {est_fpath} (ref file: {ref_fpath})"
-            )
+
+    est_ref_pairs = get_matched_files(est_dir, ref_dir)
 
     output_fhandle = (
         open(output_stats_file, "w") if output_stats_file is not None else None
@@ -104,38 +109,9 @@ def evaluate_mir_eval(est_dir, ref_dir, output_stats_file=None, est_shift=0):
         help="Path to the file to save the evaluation stats",
     )
 
-    # add mir_eval and dtw subparsers
-    subparsers = parser.add_subparsers(help="sub-command help")
-    mir_eval_parse = subparsers.add_parser(
-        "run_mir_eval",
-        help="Run standard mir_eval evaluation on MAESTRO test set.",
-    )
-    mir_eval_parse.add_argument(
-        "--shift",
-        type=int,
-        default=0,
-        help="Shift to apply to the estimated pitches.",
-    )
-
-    # to come
-    dtw_eval_parse = subparsers.add_parser(
-        "run_dtw",
-        help="Run dynamic time warping evaluation on a specified dataset.",
-    )
-
     args = parser.parse_args()
-    if not hasattr(args, "command"):
-        parser.print_help()
-        print("Unrecognized command")
-        exit(1)
-
-    # todo: should we add an option to run transcription again every time we wish to evaluate?
-    #  that way, we can run both tests with a range of different audio augmentations right here.
-    #  -> We expect that baseline methods will fall flat on these, while aria-amt will be OK.
-
-    if args.command == "run_mir_eval":
-        evaluate_mir_eval(
-            args.est_dir, args.ref_dir, args.output_stats_file, args.shift
-        )
-    elif args.command == "run_dtw":
-        pass
+    evaluate_mir_eval(
+        args.est_dir,
+        args.ref_dir,
+        args.output_stats_file,
+    )