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Bugfix: Delay pattern mask is applied twice #110

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Bugfix: Delay pattern mask is applied twice #110

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12e455a
fix: don't apply delay mask on input ids before sampling
Aug 16, 2024
6124a46
fix: set logits warper none for deterministic greedy generation
Aug 16, 2024
cc3b190
feat: example notebook for providing a voice enrolment to make voice …
Aug 16, 2024
fbaf621
feat: add main_input_name input_values to DACModel, and remove notebook
Guppy16 Sep 26, 2024
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385 changes: 385 additions & 0 deletions helpers/voice_enrolment_demo/enrol.ipynb
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I've been able to test this with the following code, which also requires a small modification of the DAC code (adding main_input_name = "input_values" as a class attribute of DACModel) :

import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, set_seed, AutoFeatureExtractor
import soundfile as sf
import torchaudio

device = "cuda" if torch.cuda.is_available() else "cpu"

model_id = "parler-tts/parler-tts-mini-v1"

model = ParlerTTSForConditionalGeneration.from_pretrained(model_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

init_audio, init_sr = torchaudio.load(PATH_TO_SPECIFY)
init_audio = torchaudio.functional.resample(init_audio, init_sr, model.config.sampling_rate)
init_audio = init_audio.mean(0)
init_prompt = "Here, write the transcript of the init audio"

prompt = "Is it really working ?"
description = "A man speaker speaks quickly with a low-pitched voice. The recording is of very high quality, with the speaker's voice sounding clear and very close up." # TODO: adapt the prompt to describe the input audio

input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(init_prompt + " " + prompt, return_tensors="pt").input_ids.to(device)
input_values = feature_extractor(init_audio, sampling_rate=model.config.sampling_rate, return_tensors="pt").input_values.to(device)
set_seed(2)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids, input_values = input_values)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)

I found that Parler has difficulty generalizing to unseen speakers (meaning using a speaker that has not been seen during training or that has not been generated by Parler), so there's no actual edge of using it for voice cloning. However, from my experiment, it's working quite well with Parler generation!

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Hey @ylacombe , I tried the above code sample with both the mini and large model but the audio file generated is noisy and inconsistent. I've used the input audio generated through ParlerTTS itself.

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This is a clean snippet! When calling model.generate(...) Is there a preference for using input_values=input_values? I was originally doing something along the lines of decoder_input_ids=input_values.squeeze().long().

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4 changes: 3 additions & 1 deletion parler_tts/modeling_parler_tts.py
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Expand Up @@ -3387,7 +3387,8 @@ def generate(
)

# build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Parler-TTS)
input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
# but don't overwrite the input_ids tensor with the delay pattern mask. We perform that later
_, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
Comment on lines +3390 to +3391
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As pointed out, this is a redundant operation that has no impact on the results!

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Hmm, I think this line does indeed change the results when using enrolled tokens. Perhaps your setup is working because it is slightly different as you've described below. I shall try this and get back to you

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Ok, so my testing shows that this fix is required to get the right audio when doing the enrolment. Here is an example audio file generated with and without the fix:
audio.zip

input_ids,
bos_token_id=generation_config._bos_token_tensor,
pad_token_id=generation_config._pad_token_tensor,
Expand Down Expand Up @@ -3442,6 +3443,7 @@ def generate(
generation_config=generation_config,
synced_gpus=synced_gpus,
streamer=streamer,
logits_warper=None,
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You should keep the logits_warper, I'm not sure why you removed it!

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I didn't remove it! Originally, logits_warper wasn't being passed in, so this part of the code was failing. I believe when doing greedy search, logits_warper=None should be set. Please could you double check this!

**model_kwargs,
)

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