Text-to-speech (TTS) with Parler-TTS and OpenVINO#

This Jupyter notebook can be launched after a local installation only.

Github

Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc). It is a reproduction of work from the paper Natural language guidance of high-fidelity text-to-speech with synthetic annotations by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively.

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Text-to-speech models trained on large-scale datasets have demonstrated impressive in-context learning capabilities and naturalness. However, control of speaker identity and style in these models typically requires conditioning on reference speech recordings, limiting creative applications. Alternatively, natural language prompting of speaker identity and style has demonstrated promising results and provides an intuitive method of control. However, reliance on human-labeled descriptions prevents scaling to large datasets.

This work bridges the gap between these two approaches. The authors propose a scalable method for labeling various aspects of speaker identity, style, and recording conditions. This method then is applied to a 45k hour dataset, which is used to train a speech language model. Furthermore, the authors propose simple methods for increasing audio fidelity, significantly outperforming recent work despite relying entirely on found data.

GitHub repository

HuggingFace page

Table of contents:

Prerequisites#

import os

os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"

%pip install -q "openvino>=2024.2.0"
%pip install -q git+https://github.com/huggingface/parler-tts.git "gradio>=4.19" transformers "torch>=2.2" --extra-index-url https://download.pytorch.org/whl/cpu
Note: you may need to restart the kernel to use updated packages.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
googleapis-common-protos 1.63.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.
mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
onnx 1.16.1 requires protobuf>=3.20.2, but you have protobuf 3.19.6 which is incompatible.
paddlepaddle 2.6.1 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.19.6 which is incompatible.
tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.
tensorflow-datasets 4.9.2 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.
tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.
tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 3.19.6 which is incompatible.
visualdl 2.5.3 requires protobuf>=3.20.0, but you have protobuf 3.19.6 which is incompatible.
Note: you may need to restart the kernel to use updated packages.

Load the original model and inference#

import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf

device = "cpu"

repo_id = "parler-tts/parler_tts_mini_v0.1"
model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
tokenizer = AutoTokenizer.from_pretrained(repo_id)

prompt = "Hey, how are you doing today?"
description = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."

input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
2024-07-13 01:23:01.501117: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-07-13 01:23:01.536383: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: resume_download is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use force_download=True.
  warnings.warn(
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
You set add_prefix_space. The tokenizer needs to be converted from the slow tokenizers
Using the model-agnostic default max_length (=2580) to control the generation length. We recommend setting max_new_tokens to control the maximum length of the generation.
import IPython.display as ipd

ipd.Audio("parler_tts_out.wav")

Convert the model to OpenVINO IR#

Let’s define the conversion function for PyTorch modules. We use ov.convert_model function to obtain OpenVINO Intermediate Representation object and ov.save_model function to save it as XML file.

import openvino as ov


def convert(model: torch.nn.Module, xml_path: str, example_input):
    xml_path = Path(xml_path)
    if not xml_path.exists():
        xml_path.parent.mkdir(parents=True, exist_ok=True)
        with torch.no_grad():
            converted_model = ov.convert_model(model, example_input=example_input)

        ov.save_model(converted_model, xml_path)

        # cleanup memory
        torch._C._jit_clear_class_registry()
        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
        torch.jit._state._clear_class_state()

In the pipeline two models are used: Text Encoder (T5EncoderModel) and Decoder (ParlerTTSDecoder). Lets convert them one by one.

from pathlib import Path


TEXT_ENCODER_OV_PATH = Path("models/text_encoder_ir.xml")


example_input = {
    "input_ids": torch.ones((1, 39), dtype=torch.int64),
}

text_encoder_ov_model = convert(model.text_encoder, TEXT_ENCODER_OV_PATH, example_input)
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: _is_quantized_training_enabled is going to be deprecated in transformers 4.39.0. Please use model.hf_quantizer.is_trainable instead
  warnings.warn(

The Decoder Model performs in generation pipeline and we can separate it into two stage. In the first stage the model generates past_key_values into output for the second stage. In the second stage the model produces tokens during several runs.

DECODER_STAGE_1_OV_PATH = Path("models/decoder_stage_1_ir.xml")


class DecoderStage1Wrapper(torch.nn.Module):
    def __init__(self, decoder):
        super().__init__()
        self.decoder = decoder

    def forward(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, prompt_hidden_states=None):
        return self.decoder(
            input_ids=input_ids,
            return_dict=False,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            prompt_hidden_states=prompt_hidden_states,
        )


example_input = {
    "input_ids": torch.ones((9, 1), dtype=torch.int64),
    "encoder_hidden_states": torch.ones((1, 39, 1024), dtype=torch.float32),
    "encoder_attention_mask": torch.ones((1, 39), dtype=torch.int64),
    "prompt_hidden_states": torch.ones((1, 9, 1024), dtype=torch.float32),
}

decoder_1_ov_model = convert(DecoderStage1Wrapper(model.decoder.model.decoder), DECODER_STAGE_1_OV_PATH, example_input)
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if input_shape[-1] > 1 or self.sliding_window is not None:
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if past_key_values_length > 0:
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:221: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if seq_len > self.weights.size(0):
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:328: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:335: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if attention_mask.size() != (bsz, 1, tgt_len, src_len):
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:367: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
DECODER_STAGE_2_OV_PATH = Path("models/decoder_stage_2_ir.xml")


class DecoderStage2Wrapper(torch.nn.Module):
    def __init__(self, decoder):
        super().__init__()
        self.decoder = decoder

    def forward(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None):
        past_key_values = tuple(tuple(past_key_values[i : i + 4]) for i in range(0, len(past_key_values), 4))
        return self.decoder(
            input_ids=input_ids,
            return_dict=False,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
        )


example_input = {
    "input_ids": torch.ones((9, 1), dtype=torch.int64),
    "encoder_hidden_states": torch.ones((1, 39, 1024), dtype=torch.float32),
    "encoder_attention_mask": torch.ones((1, 39), dtype=torch.int64),
    "past_key_values": (
        (
            torch.ones(1, 16, 10, 64, dtype=torch.float32),
            torch.ones(1, 16, 10, 64, dtype=torch.float32),
            torch.ones(1, 16, 39, 64, dtype=torch.float32),
            torch.ones(1, 16, 39, 64, dtype=torch.float32),
        )
        * 24
    ),
}

decoder_2_ov_model = convert(DecoderStage2Wrapper(model.decoder.model.decoder), DECODER_STAGE_2_OV_PATH, example_input)
/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-727/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if (

Compiling models and inference#

Select device from dropdown list for running inference using OpenVINO.

import ipywidgets as widgets

core = ov.Core()
device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="AUTO",
    description="Device:",
    disabled=False,
)

device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

Let’s create callable wrapper classes for compiled models to allow interaction with original pipeline. Note that all of wrapper classes return torch.Tensors instead of np.arrays. In the DecoderWrapper we separates the pipeline into two stages.

from collections import namedtuple

EncoderOutput = namedtuple("EncoderOutput", "last_hidden_state")
DecoderOutput = namedtuple("DecoderOutput", ("last_hidden_state", "past_key_values", "hidden_states", "attentions", "cross_attentions"))


class TextEncoderModelWrapper(torch.nn.Module):
    def __init__(self, encoder_ir_path, config):
        self.encoder = core.compile_model(encoder_ir_path, device.value)
        self.config = config
        self.dtype = self.config.torch_dtype

    def __call__(self, input_ids, **_):
        last_hidden_state = self.encoder(input_ids)[0]
        return EncoderOutput(torch.from_numpy(last_hidden_state))


class DecoderWrapper(torch.nn.Module):
    def __init__(self, decoder_stage_1_ir_path, decoder_stage_2_ir_path):
        super().__init__()
        self.decoder_stage_1 = core.compile_model(decoder_stage_1_ir_path, device.value)
        self.decoder_stage_2 = core.compile_model(decoder_stage_2_ir_path, device.value)

    def __call__(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, prompt_hidden_states=None, **kwargs):
        inputs = {}
        if input_ids is not None:
            inputs["input_ids"] = input_ids
        if encoder_hidden_states is not None:
            inputs["encoder_hidden_states"] = encoder_hidden_states
        if encoder_attention_mask is not None:
            inputs["encoder_attention_mask"] = encoder_attention_mask
        if prompt_hidden_states is not None:
            inputs["prompt_hidden_states"] = prompt_hidden_states
        if past_key_values is not None:
            past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
            inputs["past_key_values"] = past_key_values
            arguments = (
                input_ids,
                encoder_hidden_states,
                encoder_attention_mask,
                *past_key_values,
            )
            outs = self.decoder_stage_2(arguments)
        else:
            outs = self.decoder_stage_1(inputs)

        outs = [torch.from_numpy(out) for out in outs.values()]
        past_key_values = list(list(outs[i : i + 4]) for i in range(1, len(outs), 4))

        return DecoderOutput(outs[0], past_key_values, None, None, None)

Now we can replace the original models by our wrapped OpenVINO models and run inference.

model.text_encoder = TextEncoderModelWrapper(TEXT_ENCODER_OV_PATH, model.text_encoder.config)
model.decoder.model.decoder = DecoderWrapper(DECODER_STAGE_1_OV_PATH, DECODER_STAGE_2_OV_PATH)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
Using the model-agnostic default max_length (=2580) to control the generation length. We recommend setting max_new_tokens to control the maximum length of the generation.
import IPython.display as ipd

ipd.Audio("parler_tts_out.wav")