Bugfix: Delay pattern mask is applied twice

[Guppy16]

There are a few bugfixes / contributions:

1. (Bugfix) In ParlerTTSForConditionalGeneration::generate(), the delay patter mask is built and applied to input_ids before calling _sample(). However, we do not want to apply the mask until we're inside the _sample() function. The above bug doesn't affect the current inference setup because the input_ids returned happens to be the same as what's passed in.

2. (Contribution) I've provided an example of how to do audio enrolment to improve the consistency of audio generation in (helpers/voice_enrolment/enrol.ipynb ). I believe it helps, but I'm not sure if it's always better. Nonetheless, I mainly provided this as an example to demonstrate how the bugfix helps: we can try to provide the enrolment as prefix tokens in decoder_input_ids args when calling model.generate(). You should notice that without the bugfix, the audio sounds "crackly", which is because the mask has effectively been applied twice on the prefix.

3. (Bugfix) When performing deterministic greedy decoding (by passing in do_sample=False, there is bug where the logits_warper is not passed in. I believe this should just be None(?), which I've commited in this PR. Related to this, I also want to raise an issue that deterministic sampling by setting do_sample=False or temperatute=0.1 tends to generate random noise.

[Guppy16]

Perhaps the bugfixes also need to be applied in ParlerTTSForCausalLM? (I haven't touched this class so I'm not sure about it's intended use)

[Guppy16]

@ylacombe Would it be possible for you to review this?

[ylacombe]

Hey @Guppy16, thanks for opening this ! I'll take a look in the coming days!

[ylacombe]

(Bugfix) In ParlerTTSForConditionalGeneration::generate(), the delay patter mask is built and applied to input_ids before calling _sample(). However, we do not want to apply the mask until we're inside the _sample() function. The above bug doesn't affect the current inference setup because the input_ids returned happens to be the same as what's passed in.

So it's actually a redundant operation that changes nothing, right ? Just want to make sure it's not a bug. When I experiment, it seems that it doesn't change anything

[ylacombe]

Hey @Guppy16, so even though the modification you're proposing makes sense, I'm not sure that your other additions should be merged:

1. the logit wrapper modif is an error
2. I'd rather have an issue opened that explain how to do your voice enrolment thing than a notebook ! Would you like to write a guide about this and add it to the issues ? I can ping it afterwards

[ylacombe]

You should keep the logits_warper, I'm not sure why you removed it!

[Guppy16]

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!

[ylacombe]

As pointed out, this is a redundant operation that has no impact on the results!

[Guppy16]

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

[Guppy16]

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

[ylacombe]

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!

[kaushal-gawri9899]

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.

[Guppy16]

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().

[Guppy16]

Thanks a lot for reviewing this, as well as your great suggestions! I'll work on this in the coming few days.

[Guppy16]

2. I'd rather have an issue opened that explain how to do your voice enrolment thing than a notebook ! Would you like to write a guide about this and add it to the issues ? I can ping it afterwards

Looks like @apresence has made a start on this! I've added a modified version of ur snippet there (#139)