Many bug fixes for v1.1.0

CHANGELOG.md

@@ -5,10 +5,26 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 
-## [1.0.1] - TBD
+## [1.1.0a] - TBD
 
 Not using torchaudio anymore when writing audio files, relying instead directly on the commandline ffmpeg. Also not using it anymore for reading audio files, for similar reasons.
 
+Fixed DAC support with non default number of codebooks.
+
+Fixed bug when `two_step_cfg` was overriden when calling `generate()`.
+
+Fixed samples being always prompted with audio, rather than having both prompted and unprompted.
+
+**Backward incompatible change:** A `torch.no_grad` around the computation of the conditioning made its way in the public release.
+	The released models were trained without this. Those impact linear layers applied to the output of the T5 or melody conditioners.
+	We removed it, so you might need to retrain models.
+
+**Backward incompatible change:** Fixing wrong sample rate in CLAP (WARNING if you trained model with CLAP before).
+
+**Backward incompatible change:** Renamed VALLEPattern to CoarseFirstPattern, as it was wrongly named. Probably no one
+	retrained a model with this pattern, so hopefully this won't impact you!
+
+
 ## [1.0.0] - 2023-09-07
 
 Major revision, added training code for EnCodec, AudioGen, MusicGen, and MultiBandDiffusion.

audiocraft/__init__.py

@@ -23,4 +23,4 @@
 # flake8: noqa
 from . import data, modules, models
 
-__version__ = '1.0.0'
+__version__ = '1.1.0a1'

audiocraft/models/audiogen.py

@@ -38,6 +38,10 @@ def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
         self.name = name
         self.compression_model = compression_model
         self.lm = lm
+        # Just to be safe, let's put everything in eval mode.
+        self.compression_model.eval()
+        self.lm.eval()
+
         if max_duration is None:
             if hasattr(lm, 'cfg'):
                 max_duration = lm.cfg.dataset.segment_duration  # type: ignore

audiocraft/models/builders.py

@@ -23,7 +23,7 @@
     MusicLMPattern,
     ParallelPatternProvider,
     UnrolledPatternProvider,
-    VALLEPattern,
+    CoarseFirstPattern,
 )
 from ..modules.conditioners import (
     BaseConditioner,
@@ -172,7 +172,7 @@ def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> Codeb
         'parallel': ParallelPatternProvider,
         'delay': DelayedPatternProvider,
         'unroll': UnrolledPatternProvider,
-        'valle': VALLEPattern,
+        'coarse_first': CoarseFirstPattern,
         'musiclm': MusicLMPattern,
     }
     name = cfg.modeling
@@ -196,7 +196,6 @@ def get_debug_compression_model(device='cpu', sample_rate: int = 32000):
         'dimension': 32,
         'ratios': ratios,
     }
-    print(seanet_kwargs)
     encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
     decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
     quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)

audiocraft/models/encodec.py

@@ -276,7 +276,7 @@ def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
 
     def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
         codes = self.model.encode(x, self.n_quantizers)[1]
-        return codes, None
+        return codes[:, :self.n_quantizers], None
 
     def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
         assert scale is None

audiocraft/models/lm.py

@@ -314,7 +314,8 @@ def _sample_next_token(self,
                            temp: float = 1.0,
                            top_k: int = 0,
                            top_p: float = 0.0,
-                           cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
+                           cfg_coef: tp.Optional[float] = None,
+                           two_step_cfg: tp.Optional[bool] = None) -> torch.Tensor:
         """Sample next token from the model given a sequence and a set of conditions. The model supports
         multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
 
@@ -335,7 +336,8 @@ def _sample_next_token(self,
         B = sequence.shape[0]
         cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
         model = self if self._fsdp is None else self._fsdp
-        if self.two_step_cfg and cfg_conditions != {}:
+        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
+        if two_step_cfg and cfg_conditions != {}:
             assert isinstance(cfg_conditions, tuple), type(cfg_conditions)
             condition_tensors, null_condition_tensors = cfg_conditions
             cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
@@ -493,7 +495,7 @@ def generate(self,
                 # sample next token from the model, next token shape is [B, K, 1]
                 next_token = self._sample_next_token(
                     curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
-                    cfg_coef=cfg_coef)
+                    cfg_coef=cfg_coef, two_step_cfg=two_step_cfg)
                 # ensure the tokens that should be masked are properly set to special_token_id
                 # as the model never output special_token_id
                 valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)

audiocraft/models/musicgen.py

@@ -52,6 +52,10 @@ def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
         self.name = name
         self.compression_model = compression_model
         self.lm = lm
+        # Just to be safe, let's put everything in eval mode.
+        self.compression_model.eval()
+        self.lm.eval()
+
         if max_duration is None:
             if hasattr(lm, 'cfg'):
                 max_duration = lm.cfg.dataset.segment_duration  # type: ignore

audiocraft/modules/codebooks_patterns.py

@@ -486,9 +486,14 @@ def get_pattern(self, timesteps: int) -> Pattern:
         return Pattern(out, n_q=self.n_q, timesteps=timesteps)
 
 
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern.
-    We further allow some delays for the codebooks other than the first one.
+class CoarseFirstPattern(CodebooksPatternProvider):
+    """First generates all the codebooks #1 (e.g. coarser), then the remaining ones,
+    potentially with delays.
+
+    ..Warning:: You must always generate the full training duration at test time, for instance,
+        30 seconds, as otherwise, the fine codebooks will start being generated in an unexpected
+        location. This is due to the non causality of the remaining codebooks with respect to
+        the first ones.
 
     Args:
         n_q (int): Number of codebooks.

audiocraft/modules/conditioners.py

@@ -792,6 +792,8 @@ def __init__(self, dim: int, output_dim: int, device: str, attribute: str,
             import laion_clap  # type: ignore
         except ImportError:
             raise ImportError("Please install CLAP to use the CLAPEmbeddingConditioner: 'pip install laion_clap'")
+        warnings.warn("Sample rate for CLAP conditioner was fixed in version v1.1.0, (from 44.1 to 48 kHz). "
+                      "Please retrain all models.")
         checkpoint = AudioCraftEnvironment.resolve_reference_path(checkpoint)
         clap_tokenize = RobertaTokenizer.from_pretrained('roberta-base')
         clap_model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=model_arch)

audiocraft/optim/fsdp.py

@@ -143,8 +143,8 @@ def _name_without_fsdp_prefix(name: str) -> str:
         new_parts = [part for part in parts if part != FSDP_WRAPPED_MODULE]
         return '.'.join(new_parts)
 
-    def state_dict(self) -> tp.Dict[str, tp.Any]:  # type: ignore
-        state = dict(super().state_dict())
+    def state_dict(self, *args, **kwargs) -> tp.Dict[str, tp.Any]:  # type: ignore
+        state = dict(super().state_dict(*args, **kwargs))
         for key, value in list(state.items()):
             if is_sharded_tensor(value):
                 del state[key]

audiocraft/solvers/musicgen.py

@@ -7,6 +7,7 @@
 from pathlib import Path
 import time
 import typing as tp
+import warnings
 
 import flashy
 import math
@@ -226,7 +227,6 @@ def _compute_cross_entropy(
         ce = ce / K
         return ce, ce_per_codebook
 
-    @torch.no_grad()
     def _prepare_tokens_and_attributes(
         self, batch: tp.Tuple[torch.Tensor, tp.List[SegmentWithAttributes]],
         check_synchronization_points: bool = False
@@ -243,6 +243,12 @@ def _prepare_tokens_and_attributes(
                 with B the batch size, K the number of codebooks, T_s the token timesteps.
             Padding mask (torch.Tensor): Mask with valid positions in the tokens tensor, of shape [B, K, T_s].
         """
+        if self.model.training:
+            warnings.warn(
+                "Up to version 1.0.1, the _prepare_tokens_and_attributes was evaluated with `torch.no_grad()`. "
+                "This is inconsistent with how model were trained in the MusicGen paper. We removed the "
+                "`torch.no_grad()` in version 1.1.0. Small changes to the final performance are expected. "
+                "Really sorry about that.")
         if self._cached_batch_loader is None or self.current_stage != "train":
             audio, infos = batch
             audio = audio.to(self.device)
@@ -533,7 +539,7 @@ def get_hydrated_conditions(meta: tp.List[SegmentWithAttributes]):
                     rtf = 1.
                 else:
                     gen_unprompted_outputs = self.run_generate_step(
-                        batch, gen_duration=target_duration, prompt_duration=prompt_duration,
+                        batch, gen_duration=target_duration, prompt_duration=None,
                         **self.generation_params)
                     gen_unprompted_audio = gen_unprompted_outputs['gen_audio'].cpu()
                     rtf = gen_unprompted_outputs['rtf']

audiocraft/utils/cache.py

@@ -57,7 +57,7 @@ class EmbeddingCache:
             specify the index corresponding to the current embedding in the object that can represent batch metadata.
             If not specified, will return the full embedding unmodified.
     """
-    def __init__(self, cache_path: tp.Union[Path], device: tp.Union[str, torch.device],
+    def __init__(self, cache_path: tp.Union[str, Path], device: tp.Union[str, torch.device],
                  compute_embed_fn: tp.Callable[[Path, tp.Any, int], torch.Tensor],
                  extract_embed_fn: tp.Optional[tp.Callable[[torch.Tensor, tp.Any, int], torch.Tensor]] = None):
         self.cache_path = Path(cache_path)

config/conditioner/clapemb2music.yaml

@@ -23,7 +23,7 @@ conditioners:
       checkpoint: //reference/clap/music_audioset_epoch_15_esc_90.14.pt
       model_arch: 'HTSAT-base'
       enable_fusion: false
-      sample_rate: 44100
+      sample_rate: 48000
       max_audio_length: 10
       audio_stride: 1
       dim: 512

config/model/lm/audiogen_lm.yaml

@@ -18,7 +18,7 @@ codebooks_pattern:
     delays: [0, 0, 0, 0]
   music_lm:
     group_by: 2
-  valle:
+  coarse_first:
     delays: [0, 0, 0]
 
 transformer_lm:

config/model/lm/musicgen_lm.yaml

@@ -18,7 +18,7 @@ codebooks_pattern:
     delays: [0, 0, 0, 0]
   music_lm:
     group_by: 2
-  valle:
+  coarse_first:
     delays: [0, 0, 0]
 
 transformer_lm:

docs/MUSICGEN.md

@@ -143,6 +143,10 @@ We provide a dummy dataset containing just a few examples for illustrative purpo
 
 Please read first the [TRAINING documentation](./TRAINING.md), in particular the Environment Setup section.
 
+
+**Warning:** As of version 1.1.0, a few breaking changes were introduced. Check the [CHANGELOG.md](../CHANGELOG.md)
+file for more information. You might need to retrain some of your models.
+
 ### Example configurations and grids
 
 We provide configurations to reproduce the released models and our research.

tests/models/test_musicgen.py

@@ -56,3 +56,10 @@ def test_generate_long(self):
         wav = mg.generate(
             ['youpi', 'lapin dort'])
         assert list(wav.shape) == [2, 1, 32000 * 4]
+
+    def test_generate_two_step_cfg(self):
+        mg = self.get_musicgen()
+        mg.set_generation_params(duration=2.0, extend_stride=2., two_step_cfg=True)
+        wav = mg.generate(
+            ['youpi', 'lapin dort'])
+        assert list(wav.shape) == [2, 1, 64000]