beat sync w allin1,pytsmod /chordchroma denoising

Author: sakemin

__pycache__/predict.cpython-39.pyc

@@ -252,6 +252,30 @@ def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
         if return_tokens:
             return self.generate_audio(tokens), tokens
         return self.generate_audio(tokens)
+    
+    def generate_continuation_with_audio_token(self, prompt, 
+                              descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
+                              progress: bool = False, return_tokens: bool = False) \
+            -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
+        """Generate samples conditioned on audio prompts.
+
+        Args:
+            prompt (torch.Tensor): A batch of waveforms used for continuation.
+                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
+            prompt_sample_rate (int): Sampling rate of the given audio waveforms.
+            descriptions (list of str, optional): A list of strings used as text conditioning. Defaults to None.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        
+        if descriptions is None:
+            descriptions = [None] * len(prompt)
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
+        assert prompt_tokens is None
+        prompt_tokens = prompt
+        tokens = self._generate_tokens(attributes, prompt_tokens, progress)
+        if return_tokens:
+            return self.generate_audio(tokens), tokens
+        return self.generate_audio(tokens)
 
     def generate_continuation_with_audio_chroma(self, prompt: torch.Tensor, prompt_sample_rate: int, melody_wavs: MelodyType,
                              melody_sample_rate: int, descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
@@ -298,7 +322,7 @@ def generate_continuation_with_audio_chroma(self, prompt: torch.Tensor, prompt_s
             return self.generate_audio(tokens), tokens
         return self.generate_audio(tokens)
 
-    def generate_continuation_with_audio_tokens(self, prompt, melody_wavs: MelodyType,
+    def generate_continuation_with_audio_tokens_and_audio_chroma(self, prompt, melody_wavs: MelodyType,
                              melody_sample_rate: int, descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
                               progress: bool = False, return_tokens: bool = False) \
             -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
@@ -369,6 +393,33 @@ def generate_continuation_with_text_chroma(self, prompt: torch.Tensor, prompt_sa
         if return_tokens:
             return self.generate_audio(tokens), tokens
         return self.generate_audio(tokens)
+
+    def generate_continuation_with_audio_tokens_and_text_chroma(self, prompt, descriptions: tp.List[str], chord_texts: tp.Union[tp.List[str],str],
+                             progress: bool = False, bpm: tp.Union[float,int,tp.List[float],tp.List[int]] = 120, meter: tp.Optional[tp.Union[int,tp.List[int]]] = 4,
+                             return_tokens: bool = False) -> tp.Union[torch.Tensor,
+                                                                      tp.Tuple[torch.Tensor, torch.Tensor]]:
+        """Generate samples conditioned on text and melody.
+
+        Args:
+            descriptions (list of str): A list of strings used as text conditioning.
+            melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
+                melody conditioning. Should have shape [B, C, T] with B matching the description length,
+                C=1 or 2. It can be [C, T] if there is a single description. It can also be
+                a list of [C, T] tensors.
+            melody_sample_rate: (int): Sample rate of the melody waveforms.
+            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
+        """
+        
+        if isinstance(chord_texts, str):
+            chord_texts = [chord_texts]
+
+        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
+                                                                        melody_wavs=chord_texts, bpm=bpm, meter=meter)
+        prompt_tokens = prompt
+        tokens = self._generate_tokens(attributes, prompt_tokens, progress)
+        if return_tokens:
+            return self.generate_audio(tokens), tokens
+        return self.generate_audio(tokens)
 
     def generate_with_text_chroma(self, descriptions: tp.List[str], chord_texts: tp.Union[tp.List[str],str],
                              progress: bool = False, bpm: tp.Union[float,int,tp.List[float],tp.List[int]] = 120, meter: tp.Optional[tp.Union[int,tp.List[int]]] = 4,

audiocraft/__pycache__/__init__.cpython-38.pyc

@@ -39,6 +39,10 @@ def __init__(self, device, sample_rate, max_duration, chroma_len, n_chroma, winh
 
         self.chords = chords.Chords()
         self.device = device
+
+        self.denoise_window_size = 7
+        self.denoise_threshold = 0.5
+
         self.model = BTC_model(config=self.config.model).to(device)
         if os.path.isfile(self.model_file):
             checkpoint = torch.load(self.model_file)
@@ -122,7 +126,7 @@ def forward(self, wavs:torch.Tensor) -> torch.Tensor:
                             break
 
             strlines = ''.join(lines)
-
+            # print(lines)
             chroma = []
 
             count = 0
@@ -149,6 +153,19 @@ def forward(self, wavs:torch.Tensor) -> torch.Tensor:
                     chroma.append(multihot)
                     count += 1
 
-            chromas.append(torch.stack(chroma, dim=0))
-        
+            chroma = torch.stack(chroma, dim=0)
+
+            # Denoising chroma
+            kernel = torch.ones(self.denoise_window_size)/self.denoise_window_size
+
+            filtered_signals = []
+            for i in range(chroma.shape[-1]):
+                filtered_signals.append(torch.nn.functional.conv1d(chroma[...,i].unsqueeze(0),
+                                                        kernel.unsqueeze(0).unsqueeze(0).to(chroma.device), 
+                                                        padding=(self.denoise_window_size - 1) // 2))
+            filtered_signals = torch.stack(filtered_signals, dim=-1)
+            filtered_signals = filtered_signals > self.denoise_threshold
+
+            chromas.append(filtered_signals.squeeze(0))
+
         return torch.stack(chromas, dim=0).to(self.device)

audiocraft/__pycache__/environment.cpython-38.pyc

@@ -842,7 +842,8 @@ def _compute_wav_embedding(self, wav: torch.Tensor, sample_rate: int) -> torch.T
         if wav.shape[-1] == 1:
             # print("1515151")
             return self._extract_chroma(wav)
-        stems = self._get_stemmed_wav(wav, sample_rate)
+        # stems = wav # Not separating the tracks
+        stems = self._get_stemmed_wav(wav, sample_rate) 
         chroma = self._extract_chroma(stems)
         # print("2727272")
         return chroma

audiocraft/adversarial/__pycache__/__init__.cpython-38.pyc

@@ -9,7 +9,7 @@ build:
   # a list of ubuntu apt packages to install
   system_packages:
     - "ffmpeg"
-    - "portaudio19-dev"
+    # - "portaudio19-dev"
     # - "libgl1-mesa-glx"
     # - "libglib2.0-0"
 
@@ -26,7 +26,6 @@ build:
     - "hydra_colorlog"
     - "julius"
     - "num2words"
-    - "numpy"
     - "sentencepiece"
     - "spacy==3.5.2"
     - "torchaudio>=2.0.0"
@@ -43,23 +42,13 @@ build:
     - "tensorboard>=1.15"
     - "pyaudio==0.2.13"
     - "git+https://github.com/CPJKU/madmom"
-    - "BeatNet==1.1.0"
-    - "contourpy==1.0.7"
-    - "cycler==0.11.0"
-    - "fonttools==4.38.0"
-    - "kiwisolver==1.4.4"
-    - "matplotlib==3.6.3"
-    # - "numpy==1.24.2"
-    - "packaging==23.0"
-    - "Pillow==9.4.0"
-    - "pyparsing==3.0.9"
-    - "python-dateutil==2.8.2"
-    - "PyWavelets==1.4.1"
-    - "scipy==1.10.0"
-    - "six==1.16.0"
+    - "ninja"
+    - "allin1"
+    - "pytsmod"
 
   # commands run after the environment is setup
-  # run:
+  run:
+    - "pip3 install natten -f https://shi-labs.com/natten/wheels/cu117/torch2.0.0/index.html"
     # - "apt-get update && apt-get install -y ffmpeg"
     # - "apt-get install unzip"
     # - "python -m pip install pip --upgrade"