After training some epochs, I only get some noise.

[Leezp99]

Thank you for your excellent work!
I'm trying to train some models off of librispeech-all(1000+hours) by using my trainer. But after training some epochs, i still get some clumsy and noisy sound. i don't know what the problem is, and how can i solve it.

model i used:

model = DiffusionModel(
    net_t=UNetV0, # The model type used for diffusion (U-Net V0 in this case)
    in_channels=1, # U-Net: number of input/output (audio) channels
    channels=[8, 32, 64, 128, 256, 512, 512, 1024, 1024], # U-Net: channels at each layer
    factors=[1, 4, 2, 2, 2, 2, 2, 2, 2], # U-Net: downsampling and upsampling factors at each layer
    items=[1, 2, 2, 2, 2, 2, 2, 4, 4], # U-Net: number of repeating items at each layer
    attentions=[0, 0, 0, 0, 0, 1, 1, 1, 1], # U-Net: attention enabled/disabled at each layer
    attention_heads=8, # U-Net: number of attention heads per attention item
    attention_features=64, # U-Net: number of attention features per attention item
    diffusion_t=VDiffusion, # The diffusion method used
    sampler_t=VSampler, # The diffusion sampler used        
    use_text_conditioning=True,  # U-Net: enables text conditioning (default T5-base)
    use_embedding_cfg=True,  # U-Net: enables classifier free guidance
    embedding_max_length=64,  # U-Net: text embedding maximum length (default for T5-base)
    embedding_features=768,  # U-Net: text mbedding features (default for T5-base)
    cross_attentions=[0, 0, 0, 1, 1, 1, 1, 1, 1],  # U-Net: cross-attention enabled/disabled at each layer
).cuda()

input speech length 2**16,
the model loss function appears to work correctly, and it can be gradually reduced.
But, the audio it sample from the model, it sounds like noise, but better than noise( I can hear someone saying in a very faint and noisy voice)
i want to ask How many epochs did you train with what dataset?