Add applause augmentation (#15)

* add data aug and clean

* fix reverb

.gitignore

@@ -15,10 +15,6 @@
 *.xml
 *.html
 *.htm
-*.mid
-*.midi
-*.wav
-*.mp3
 
 .idea/
 

amt/audio.py

@@ -182,6 +182,7 @@ def log_mel_spectrogram(
     return log_spec
 
 
+# Refactor default params are stored in config.json
 class AudioTransform(torch.nn.Module):
     def __init__(
         self,
@@ -190,10 +191,12 @@ def __init__(
         max_snr: int = 50,
         max_dist_gain: int = 25,
         min_dist_gain: int = 0,
-        # ratios for the reduction of the audio quality
-        distort_ratio: float = 0.2,
-        reduce_ratio: float = 0.2,
-        spec_aug_ratio: float = 0.2,
+        noise_ratio: float = 0.95,
+        reverb_ratio: float = 0.95,
+        applause_ratio: float = 0.01,  # CHANGE
+        distort_ratio: float = 0.15,
+        reduce_ratio: float = 0.01,
+        spec_aug_ratio: float = 0.25,
     ):
         super().__init__()
         self.tokenizer = AmtTokenizer()
@@ -208,9 +211,13 @@ def __init__(
         self.chunk_len = self.config["chunk_len"]
         self.num_samples = self.sample_rate * self.chunk_len
 
-        self.dist_ratio = distort_ratio
+        self.noise_ratio = noise_ratio
+        self.reverb_ratio = reverb_ratio
+        self.applause_ratio = applause_ratio
+        self.distort_ratio = distort_ratio
         self.reduce_ratio = reduce_ratio
         self.spec_aug_ratio = spec_aug_ratio
+        self.reduction_resample_rate = 6000  # Hardcoded?
 
         # Audio aug
         impulse_paths = self._get_paths(
@@ -219,6 +226,9 @@ def __init__(
         noise_paths = self._get_paths(
             os.path.join(os.path.dirname(__file__), "assets", "noise")
         )
+        applause_paths = self._get_paths(
+            os.path.join(os.path.dirname(__file__), "assets", "applause")
+        )
 
         # Register impulses and noises as buffers
         self.num_impulse = 0
@@ -231,6 +241,11 @@ def __init__(
             self.register_buffer(f"noise_{i}", noise)
             self.num_noise += 1
 
+        self.num_applause = 0
+        for i, applause in enumerate(self._get_noise(applause_paths)):
+            self.register_buffer(f"applause_{i}", applause)
+            self.num_applause += 1
+
         self.spec_transform = torchaudio.transforms.Spectrogram(
             n_fft=self.config["n_fft"],
             hop_length=self.config["hop_len"],
@@ -321,15 +336,37 @@ def apply_noise(self, wav: torch.tensor):
 
         return AF.add_noise(waveform=wav, noise=noise, snr=snr_dbs)
 
+    def apply_applause(self, wav: torch.tensor):
+        batch_size, _ = wav.shape
+
+        snr_dbs = torch.tensor(
+            [random.randint(1, self.min_snr) for _ in range(batch_size)]
+        ).to(wav.device)
+        applause_type = random.randint(5, self.num_applause - 1)
+
+        applause = getattr(self, f"applause_{applause_type}")
+
+        return AF.add_noise(waveform=wav, noise=applause, snr=snr_dbs)
+
     def apply_reduction(self, wav: torch.tensor):
         """
         Limit the high-band pass filter, the low-band pass filter and the sample rate
         Designed to mimic the effect of recording on a low-quality microphone or phone.
         """
-        wav = AF.highpass_biquad(wav, self.sample_rate, cutoff_freq=1200)
-        wav = AF.lowpass_biquad(wav, self.sample_rate, cutoff_freq=1400)
-        resample_rate = 6000
-        return AF.resample(wav, orig_freq=self.sample_rate, new_freq=resample_rate, lowpass_filter_width=3)
+        wav = AF.highpass_biquad(wav, self.sample_rate, cutoff_freq=300)
+        wav = AF.lowpass_biquad(wav, self.sample_rate, cutoff_freq=3400)
+        wav_downsampled = AF.resample(
+            wav,
+            orig_freq=self.sample_rate,
+            new_freq=self.reduction_resample_rate,
+            lowpass_filter_width=3,
+        )
+
+        return AF.resample(
+            wav_downsampled,
+            self.reduction_resample_rate,
+            self.sample_rate,
+        )
 
     def apply_distortion(self, wav: torch.tensor):
         gain = random.randint(self.min_dist_gain, self.max_dist_gain)
@@ -363,20 +400,23 @@ def shift_spec(self, specs: torch.Tensor, shift: int):
         return shifted_specs
 
     def aug_wav(self, wav: torch.Tensor):
-        """
-        pipeline for audio augmentation:
-            1. apply noise
-            2. apply distortion (x% of the time)
-            3. apply reduction (x% of the time)
-            4. apply reverb
-        """
+        # Noise
+        if random.random() < self.noise_ratio:
+            wav = self.apply_noise(wav)
+        if random.random() < self.applause_ratio:
+            wav = self.apply_applause(wav)
 
-        wav = self.apply_noise(wav)
-        if random.random() < self.dist_ratio:
-            wav = self.apply_distortion(wav)
+        # Distortion
         if random.random() < self.reduce_ratio:
             wav = self.apply_reduction(wav)
-        return self.apply_reverb(wav)
+        elif random.random() < self.distort_ratio:
+            wav = self.apply_distortion(wav)
+
+        # Reverb
+        if random.random() < self.reverb_ratio:
+            return self.apply_reverb(wav)
+        else:
+            return wav
 
     def norm_mel(self, mel_spec: torch.Tensor):
         log_spec = torch.clamp(mel_spec, min=1e-10).log10()
@@ -399,13 +439,13 @@ def log_mel(self, wav: torch.Tensor, shift: int | None = None):
         return log_spec
 
     def forward(self, wav: torch.Tensor, shift: int = 0):
-        # noise, distortion, reduction and reverb
+        # Noise, distortion, and reverb
         wav = self.aug_wav(wav)
 
         # Spec & pitch shift
         log_mel = self.log_mel(wav, shift)
 
-        # Spec aug in 20% of the cases
+        # Spec aug
         if random.random() < self.spec_aug_ratio:
             log_mel = self.spec_aug(log_mel)
 

amt/evaluate.py

@@ -6,6 +6,7 @@
 import json
 import os
 
+
 def midi_to_intervals_and_pitches(midi_file_path):
     """
     This function reads a MIDI file and extracts note intervals and pitches
@@ -55,18 +56,26 @@ def evaluate_mir_eval(est_dir, ref_dir, output_stats_file=None, est_shift=0):
         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")))
+            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})")
+            print(
+                f"Reference file not found for {est_fpath} (ref file: {ref_fpath})"
+            )
 
-    output_fhandle = open(output_stats_file, "w") if output_stats_file is not None else None
+    output_fhandle = (
+        open(output_stats_file, "w") if output_stats_file is not None else None
+    )
 
     for est_file, ref_file in tqdm(est_ref_pairs):
         ref_intervals, ref_pitches = midi_to_intervals_and_pitches(ref_file)
         est_intervals, est_pitches = midi_to_intervals_and_pitches(est_file)
         ref_pitches_hz = midi_to_hz(ref_pitches)
         est_pitches_hz = midi_to_hz(est_pitches, est_shift)
-        scores = mir_eval.transcription.evaluate(ref_intervals, ref_pitches_hz, est_intervals, est_pitches_hz)
+        scores = mir_eval.transcription.evaluate(
+            ref_intervals, ref_pitches_hz, est_intervals, est_pitches_hz
+        )
         if output_fhandle is not None:
             output_fhandle.write(json.dumps(scores))
             output_fhandle.write("\n")
@@ -76,30 +85,43 @@ def evaluate_mir_eval(est_dir, ref_dir, output_stats_file=None, est_shift=0):
 
 if __name__ == "__main__":
     import argparse
+
     parser = argparse.ArgumentParser(usage="evaluate <command> [<args>]")
     parser.add_argument(
         "--est-dir",
         type=str,
-        help="Path to the directory containing either the transcribed MIDI files or WAV files to be transcribed."
+        help="Path to the directory containing either the transcribed MIDI files or WAV files to be transcribed.",
     )
     parser.add_argument(
         "--ref-dir",
         type=str,
-        help="Path to the directory containing the reference files (we'll use gold MIDI for mir_eval, WAV for dtw)."
+        help="Path to the directory containing the reference files (we'll use gold MIDI for mir_eval, WAV for dtw).",
     )
     parser.add_argument(
-        '--output-stats-file',
+        "--output-stats-file",
         default=None,
-        type=str, help="Path to the file to save the evaluation stats"
+        type=str,
+        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.")
+    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.")
+    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"):
@@ -112,6 +134,8 @@ def evaluate_mir_eval(est_dir, ref_dir, output_stats_file=None, est_shift=0):
     #  -> 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)
+        evaluate_mir_eval(
+            args.est_dir, args.ref_dir, args.output_stats_file, args.shift
+        )
     elif args.command == "run_dtw":
         pass

amt/infer.py

@@ -23,6 +23,7 @@
 
 # TODO: Profile and fix gpu util
 
+
 def calculate_vel(
     logits: torch.Tensor,
     init_vel: int,
@@ -89,6 +90,8 @@ def calculate_onset(
 
 from functools import wraps
 from torch.cuda import is_bf16_supported
+
+
 def optional_bf16_autocast(func):
     @wraps(func)
     def wrapper(*args, **kwargs):
@@ -100,6 +103,7 @@ def wrapper(*args, **kwargs):
             # Call the function with float16 if bfloat16 is not supported
             with torch.autocast("cuda", dtype=torch.float16):
                 return func(*args, **kwargs)
+
     return wrapper
 
 

amt/run.py

@@ -25,19 +25,25 @@ def _add_maestro_args(subparser):
 
 def _add_transcribe_args(subparser):
     subparser.add_argument("model_name", help="name of model config file")
-    subparser.add_argument('checkpoint_path', help="checkpoint path")
-    subparser.add_argument("-load_path", help="path to mp3/wav file", required=False)
+    subparser.add_argument("checkpoint_path", help="checkpoint path")
+    subparser.add_argument(
+        "-load_path", help="path to mp3/wav file", required=False
+    )
     subparser.add_argument(
         "-load_dir", help="dir containing mp3/wav files", required=False
     )
-    subparser.add_argument("-save_dir", help="dir to save midi files", required=True)
+    subparser.add_argument(
+        "-save_dir", help="dir to save midi files", required=True
+    )
     subparser.add_argument(
         "-multi_gpu", help="use all GPUs", action="store_true", default=False
     )
     subparser.add_argument("-bs", help="batch size", type=int, default=16)
 
 
-def build_maestro(maestro_dir, maestro_csv_file, train_file, val_file, test_file, num_procs):
+def build_maestro(
+    maestro_dir, maestro_csv_file, train_file, val_file, test_file, num_procs
+):
     from amt.data import AmtDataset
 
     assert os.path.isdir(maestro_dir), "MAESTRO directory not found"
@@ -101,9 +107,14 @@ def build_maestro(maestro_dir, maestro_csv_file, train_file, val_file, test_file
 
 
 def transcribe(
-        model_name, checkpoint_path, save_dir, load_path=None, load_dir=None,
-        batch_size=16, multi_gpu=False,
-        augment=None,
+    model_name,
+    checkpoint_path,
+    save_dir,
+    load_path=None,
+    load_dir=None,
+    batch_size=16,
+    multi_gpu=False,
+    augment=None,
 ):
     """
     Transcribe audio files to midi using the given model and checkpoint.
@@ -139,9 +150,7 @@ def transcribe(
     assert os.path.isfile(checkpoint_path), "model checkpoint file not found"
     assert load_path or load_dir, "must give either load path or dir"
     if load_path:
-        assert os.path.isfile(
-            load_path
-        ), f"audio file not found: {load_path}"
+        assert os.path.isfile(load_path), f"audio file not found: {load_path}"
         trans_mode = "single"
     if load_dir:
         assert os.path.isdir(load_dir), "load directory doesn't exist"
@@ -232,8 +241,12 @@ def main():
     parser = argparse.ArgumentParser(usage="amt <command> [<args>]")
     subparsers = parser.add_subparsers(help="sub-command help")
     # add maestro and transcribe subparsers
-    subparser_maestro = subparsers.add_parser("maestro", help="Commands to build the maestro dataset.")
-    subparser_transcribe = subparsers.add_parser("transcribe", help="Commands to run transcription.")
+    subparser_maestro = subparsers.add_parser(
+        "maestro", help="Commands to build the maestro dataset."
+    )
+    subparser_transcribe = subparsers.add_parser(
+        "transcribe", help="Commands to run transcription."
+    )
     _add_maestro_args(subparser_maestro)
     _add_transcribe_args(subparser_transcribe)