Update inference (#22)

* synth data - untested

* adjust audio striding

* add detune aug

* add aug val to train

* add b64 encoding to dataset

* update dataset

* fix

* training changes

* inference changes

* format

* format

amt/audio.py

@@ -194,10 +194,11 @@ def __init__(
         noise_ratio: float = 0.95,
         reverb_ratio: float = 0.95,
         applause_ratio: float = 0.01,
-        bandpass_ratio: float = 0.1,
+        bandpass_ratio: float = 0.15,
         distort_ratio: float = 0.15,
         reduce_ratio: float = 0.01,
-        codecs_ratio: float = 0.01,
+        detune_ratio: float = 0.1,
+        detune_max_shift: float = 0.15,
         spec_aug_ratio: float = 0.5,
     ):
         super().__init__()
@@ -219,8 +220,9 @@ def __init__(
         self.bandpass_ratio = bandpass_ratio
         self.distort_ratio = distort_ratio
         self.reduce_ratio = reduce_ratio
+        self.detune_ratio = detune_ratio
+        self.detune_max_shift = detune_max_shift
         self.spec_aug_ratio = spec_aug_ratio
-        self.codecs_ratio = codecs_ratio
         self.reduction_resample_rate = 6000  # Hardcoded?
 
         # Audio aug
@@ -268,6 +270,19 @@ def __init__(
             ),
         )
 
+    def get_params(self):
+        return {
+            "noise_ratio": self.noise_ratio,
+            "reverb_ratio": self.reverb_ratio,
+            "applause_ratio": self.applause_ratio,
+            "bandpass_ratio": self.bandpass_ratio,
+            "distort_ratio": self.distort_ratio,
+            "reduce_ratio": self.reduce_ratio,
+            "detune_ratio": self.detune_ratio,
+            "detune_max_shift": self.detune_max_shift,
+            "spec_aug_ratio": self.spec_aug_ratio,
+        }
+
     def _get_paths(self, dir_path):
         os.makedirs(dir_path, exist_ok=True)
 
@@ -399,21 +414,7 @@ def distortion_aug_cpu(self, wav: torch.Tensor):
 
         return wav
 
-    def apply_codec(self, wav: torch.tensor):
-        """
-        Apply different audio codecs to the audio.
-        """
-        format_encoder_pairs = [
-            ("wav", "pcm_mulaw"),
-            ("g722", None),
-            ("ogg", "vorbis")
-        ]
-        for format, encoder in format_encoder_pairs:
-            encoder = torchaudio.io.AudioEffector(format=format, encoder=encoder)
-            if random.random() < self.codecs_ratio:
-                wav = encoder.apply(wav, self.sample_rate)
-
-    def shift_spec(self, specs: torch.Tensor, shift: int):
+    def shift_spec(self, specs: torch.Tensor, shift: int | float):
         if shift == 0:
             return specs
 
@@ -438,9 +439,21 @@ def shift_spec(self, specs: torch.Tensor, shift: int):
 
         return shifted_specs
 
+    def detune_spec(self, specs: torch.Tensor):
+        if random.random() < self.detune_ratio:
+            detune_shift = random.uniform(
+                -self.detune_max_shift, self.detune_max_shift
+            )
+            detuned_specs = self.shift_spec(specs, shift=detune_shift)
+
+            return (specs + detuned_specs) / 2
+        else:
+            return specs
+
     def aug_wav(self, wav: torch.Tensor):
         # This function doesn't apply distortion. If distortion is desired it
-        # should be run before hand on the cpu with distortion_aug_cpu.
+        # should be run beforehand on the cpu with distortion_aug_cpu. Note
+        # also that detuning is done to the spectrogram in log_mel, not the wav.
 
         # Noise
         if random.random() < self.noise_ratio:
@@ -468,10 +481,17 @@ def norm_mel(self, mel_spec: torch.Tensor):
 
         return log_spec
 
-    def log_mel(self, wav: torch.Tensor, shift: int | None = None):
+    def log_mel(
+        self, wav: torch.Tensor, shift: int | None = None, detune: bool = False
+    ):
         spec = self.spec_transform(wav)[..., :-1]
-        if shift and shift != 0:
+
+        if shift is not None and shift != 0:
             spec = self.shift_spec(spec, shift)
+        elif detune is True:
+            # Don't detune and spec shift at the same time
+            spec = self.detune_spec(spec)
+
         mel_spec = self.mel_transform(spec)
 
         # Norm
@@ -483,8 +503,8 @@ def forward(self, wav: torch.Tensor, shift: int = 0):
         # Noise, and reverb
         wav = self.aug_wav(wav)
 
-        # Spec & pitch shift
-        log_mel = self.log_mel(wav, shift)
+        # Spec, detuning & pitch shift
+        log_mel = self.log_mel(wav, shift, detune=True)
 
         # Spec aug
         if random.random() < self.spec_aug_ratio: