adapts code to train with trajectories in 1D

README.md

 # DiffWave
 **Note**  
-This code is an adaptation of the original work by the LMNT team. [Original repo link](https://img.shields.io/github/license/lmnt-com/diffwave); the version used is 0.17.   
+This code is an adaptation of the original work by the LMNT team. [Original repo link](https://img.shields.io/github/license/lmnt-com/diffwave); version 0.17.   
 
 **Part of the original README follows here:**    
   
@@ -39,6 +39,12 @@ tensorboard --logdir /path/to/model/dir --bind_all
 
 You should expect to hear intelligible (but noisy) speech by ~8k steps (~1.5h on a 2080 Ti).
 
+#### Training with trajectories
+You don't need to run any preprocessing.
+```
+python src/diffwave/__main__.py /path/to/model/dir /path/to/file/containing/trajectories --data_type trajectories_x #or trajectories if you want to use 3D data
+```
+
 #### Multi-GPU training
 By default, this implementation uses as many GPUs in parallel as returned by [`torch.cuda.device_count()`](https://pytorch.org/docs/stable/cuda.html#torch.cuda.device_count). You can specify which GPUs to use by setting the [`CUDA_DEVICES_AVAILABLE`](https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/) environment variable before running the training module.
 

src/diffwave/params.py

@@ -39,8 +39,8 @@ params = AttrDict(
     max_grad_norm=None,
 
     # Data params
+    # these are actually not used for trajectories
     sample_rate=2000,
-    # these are actually not used
     n_mels=80,
     n_fft=1024,
     hop_samples=256,
@@ -49,7 +49,7 @@ params = AttrDict(
     # Model params
     residual_layers=8,
     residual_channels=32,
-    dilation_cycle_length=8, # with this config and residual layers = 10, we get r=2047
+    dilation_cycle_length=8, # with this config and residual layers = 8, we get r=511*T
     unconditional = True,
     noise_schedule=np.linspace(1e-4, 0.01, 200).tolist(), # last param is num_timesteps
     inference_noise_schedule=[1e-4, 0.001, 0.01, 0.05, 0.2, 0.5], # for fast sampling