(commented-out) support for facebookresearch/AudioDec, but support really didn't wow me (so I commented it out until I figure out why my output audio is super crusty with AudioDec)

vall_e/config.py

@@ -776,6 +776,9 @@ class Config(BaseConfig):
 		self.models = [ Model(**model) for model in self.models ]
 		self.loras = [ LoRA(**lora) for lora in self.loras ]
 
+		if not self.models:
+			self.models = [ Model() ]
+
 		for model in self.models:
 			if not isinstance( model.experimental, dict ):
 				continue

vall_e/emb/qnt.py

@@ -89,6 +89,18 @@ try:
 except Exception as e:
 	cfg.inference.use_dac = False
 	print(str(e))
+
+"""
+# uses https://github.com/facebookresearch/AudioDec/
+# I have set up a pip-ify'd version with the caveat of having to manually handle downloading the checkpoints with a wget + unzip
+# I was not happy with testing, it sounded rather mediocre.
+try:
+	from audiodec.utils.audiodec import AudioDec, assign_model as _audiodec_assign_model
+except Exception as e:
+	cfg.inference.use_audiodec = False
+	print(str(e))
+"""
+
 @cache
 def _load_encodec_model(device="cuda", levels=cfg.model.max_levels):
 	assert cfg.sample_rate == 24_000
@@ -146,7 +158,7 @@ def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
 	kwargs = dict(model_type="44khz",model_bitrate="8kbps",tag="latest")
 	if not cfg.variable_sample_rate:
 		# yes there's a better way, something like f'{cfg.sample.rate//1000}hz'
-		if cfg.sample_rate == 44_000:
+		if cfg.sample_rate == 44_000 or cfg.sample_rate == 44_100:
 			kwargs["model_type"] = "44khz"
 		elif cfg.sample_rate == 16_000:
 			kwargs["model_type"] = "16khz"
@@ -157,10 +169,7 @@ def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
 	model = model.to(device)
 	model = model.eval()
 
-	# extra metadata
+	# to revisit later, but experiments shown that this is a bad idea
-
-	# since DAC moreso models against waveforms, we can actually use a smaller sample rate
-	# updating it here will affect the sample rate the waveform is resampled to on encoding
 	if cfg.variable_sample_rate:
 		model.sample_rate = cfg.sample_rate
 
@@ -169,8 +178,29 @@ def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
 
 	return model
 
+"""
+@cache
+def _load_audiodec_model(device="cuda", model_name=None, levels=cfg.model.max_levels):
+	if not model_name:
+		model_name = "libritts_v1" if cfg.sample_rate == 24_000 else "vctk_v1"
+	sample_rate, encoder_checkpoint, decoder_checkpoint = _audiodec_assign_model(model_name)
+
+	model = AudioDec(tx_device=device , rx_device=device )
+	model.load_transmitter(encoder_checkpoint)
+	model.load_receiver(encoder_checkpoint, decoder_checkpoint)
+
+	model.backend = "audiodec"
+	model.sample_rate = sample_rate
+
+	return model
+"""
+
 @cache
 def _load_model(device="cuda", backend=cfg.audio_backend, levels=cfg.model.max_levels):
+	"""
+	if backend == "audiodec":
+		return _load_audiodec_model(device, levels=levels)
+	"""
 	if backend == "dac":
 		return _load_dac_model(device, levels=levels)
 	if backend == "vocos":
@@ -202,6 +232,15 @@ def decode(codes: Tensor, device="cuda", levels=cfg.model.max_levels, metadata=N
 	# load the model
 	model = _load_model(device, levels=levels)
 
+	# AudioDec uses a different pathway
+	"""
+	if model.backend == "audiodec":
+		codes = codes.to( device=device )[0]
+		zq = model.rx_encoder.lookup( codes )
+		wav = model.decoder.decode(zq).squeeze(1)
+		return wav, model.sample_rate
+	"""
+
 	# DAC uses a different pathway
 	if model.backend == "dac":
 		dummy = False
@@ -233,7 +272,6 @@ def decode(codes: Tensor, device="cuda", levels=cfg.model.max_levels, metadata=N
 		# to-do: inject the sample rate encoded at, because we can actually decouple		
 		return CodecMixin_decompress(model, artifact, verbose=False).audio_data[0], artifact.sample_rate
 
-
 	kwargs = {}
 	if model.backend == "vocos":
 		x = model.codes_to_features(codes[0])
@@ -266,6 +304,9 @@ def _replace_file_extension(path, suffix):
 # an experimental way to include "trained" embeddings from the audio backend itself
 # > b-but why not just initialize the embedding weights to these instead of fetching them at r-runtime
 # each audio backend does their "embeddings" a different way that isn't just a embedding weights
+#
+# this is overkill and I don't feel like this benefits anything, but it was an idea I had
+# this only really works if the embedding dims match, and either a Linear to rescale would be needed or semi-erroneously just padding with 0s
 @torch.inference_mode()
 def encode_as_embedding(codes: Tensor, quant_level: int = 0, sums=False, device="cuda"):
 	model = _load_model(device)
@@ -320,6 +361,8 @@ def encode_as_embedding(codes: Tensor, quant_level: int = 0, sums=False, device=
 
 @torch.inference_mode()
 def encode(wav: Tensor, sr: int = cfg.sample_rate, device="cuda", levels=cfg.model.max_levels, return_metadata=True):
+
+	# DAC uses a different pathway
 	if cfg.audio_backend == "dac":
 		model = _load_dac_model(device, levels=levels )
 		signal = AudioSignal(wav, sample_rate=sr)
@@ -330,7 +373,20 @@ def encode(wav: Tensor, sr: int = cfg.sample_rate, device="cuda", levels=cfg.mod
 		with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
 			artifact = model.compress(signal, win_duration=None, verbose=False, n_quantizers=levels)
 
+	# AudioDec uses a different pathway
+	"""
+	if cfg.audio_backend == "audiodec":
+		model = _load_audiodec_model(device, levels=levels )
+		wav = wav.unsqueeze(0)
+		wav = convert_audio(wav, sr, model.sample_rate, 1)
+		wav = wav.to(device)
+
+		# wav = rearrange(wav, "t c -> t 1 c").to(device)
+		encoded = model.tx_encoder.encode(wav)
+		quantized = model.tx_encoder.quantize(encoded)
+		return quantized
 		return artifact.codes if not return_metadata else artifact
+	"""
 
 	# vocos does not encode wavs to encodecs, so just use normal encodec
 	model = _load_encodec_model(device, levels=levels)
@@ -431,3 +487,18 @@ def merge_audio( *args, device="cpu", scale=[], levels=cfg.model.max_levels ):
 
 	combined = sum(decoded) / len(decoded)
 	return encode(combined, cfg.sample_rate, device="cpu", levels=levels)[0].t()
+
+"""
+if __name__ == "__main__":
+	from vall_e.emb.qnt import encode, decode, cfg
+
+	cfg.sample_rate = 48_000
+	cfg.audio_backend = "audiodec"
+
+	wav, sr = torchaudio.load("in.wav")
+	codes = encode( wav, sr ).t()
+	print( "ENCODED:", codes.shape, codes )
+	wav, sr = decode( codes )
+	print( "DECODED:", wav.shape, wav )
+	torchaudio.save("out.wav", wav.cpu(), sr)
+"""