infer_onnx.py

import numpy as np
import onnxruntime

from text import text_to_sequence, sequence_to_text
import torch
import gradio as gr
import soundfile as sf
import tempfile
import yaml
import json
import os

from huggingface_hub import hf_hub_download
from time import perf_counter

DEFAULT_SPEAKER_ID = os.environ.get("DEFAULT_SPEAKER_ID", default="caf_08106")

def intersperse(lst, item):
    result = [item] * (len(lst) * 2 + 1)
    result[1::2] = lst
    return result


def process_text(i: int, text: str, device: torch.device):
    print(f"[{i}] - Input text: {text}")
    x = torch.tensor(
        intersperse(text_to_sequence(text, ["catalan_cleaners"]), 0),
        dtype=torch.long,
        device=device,
    )[None]
    x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
    x_phones = sequence_to_text(x.squeeze(0).tolist())
    print(x_phones)
    return x.numpy(), x_lengths.numpy()

MODEL_PATH_MATCHA_MEL=hf_hub_download(repo_id="BSC-LT/matcha-tts-cat-multispeaker", filename="matcha_multispeaker_cat_opset_15_10_steps_2399.onnx")
MODEL_PATH_MATCHA="matcha_hifigan_multispeaker_cat.onnx"
MODEL_PATH_VOCOS=hf_hub_download(repo_id="BSC-LT/vocos-mel-22khz-cat", filename="mel_spec_22khz_cat.onnx")
CONFIG_PATH=hf_hub_download(repo_id="BSC-LT/vocos-mel-22khz-cat", filename="config.yaml")
SPEAKER_ID_DICT="spk_to_id.json"

sess_options = onnxruntime.SessionOptions()
model_matcha_mel= onnxruntime.InferenceSession(str(MODEL_PATH_MATCHA_MEL), sess_options=sess_options, providers=["CPUExecutionProvider"])
model_vocos = onnxruntime.InferenceSession(str(MODEL_PATH_VOCOS), sess_options=sess_options, providers=["CPUExecutionProvider"])
#model_matcha = onnxruntime.InferenceSession(str(MODEL_PATH_MATCHA), sess_options=sess_options, providers=["CPUExecutionProvider"])

speaker_id_dict = json.load(open(SPEAKER_ID_DICT))
speakers = [sp for sp in speaker_id_dict.keys()]
speakers.sort()

def vocos_inference(mel,denoise):

    with open(CONFIG_PATH, "r") as f:
        config = yaml.safe_load(f)

    params = config["feature_extractor"]["init_args"]
    sample_rate = params["sample_rate"]
    n_fft= params["n_fft"]
    hop_length= params["hop_length"]
    win_length = n_fft

    # ONNX inference
    mag, x, y = model_vocos.run(
        None,
        {
            "mels": mel
        },
    )

    # complex spectrogram from vocos output
    spectrogram = mag * (x + 1j * y)
    window = torch.hann_window(win_length)

    if denoise:
        # Vocoder bias
        mel_rand = torch.zeros_like(torch.tensor(mel))
        mag_bias, x_bias, y_bias = model_vocos.run(
            None,
            {
                "mels": mel_rand.float().numpy()
            },
        )

        # complex spectrogram from vocos output
        spectrogram_bias = mag_bias * (x_bias + 1j * y_bias)

        # Denoising
        spec = torch.view_as_real(torch.tensor(spectrogram))
        # get magnitude of vocos spectrogram
        mag_spec = torch.sqrt(spec.pow(2).sum(-1))

        # get magnitude of bias spectrogram
        spec_bias = torch.view_as_real(torch.tensor(spectrogram_bias))
        mag_spec_bias = torch.sqrt(spec_bias.pow(2).sum(-1))

        # substract 
        strength = 0.0025
        mag_spec_denoised = mag_spec - mag_spec_bias * strength
        mag_spec_denoised = torch.clamp(mag_spec_denoised, 0.0)

        # return to complex spectrogram from magnitude
        angle = torch.atan2(spec[..., -1], spec[..., 0] )
        spectrogram = torch.complex(mag_spec_denoised * torch.cos(angle), mag_spec_denoised * torch.sin(angle))

    # Inverse stft
    pad = (win_length - hop_length) // 2
    spectrogram = torch.tensor(spectrogram)
    B, N, T = spectrogram.shape

    print("Spectrogram synthesized shape", spectrogram.shape)
    # Inverse FFT
    ifft = torch.fft.irfft(spectrogram, n_fft, dim=1, norm="backward")
    ifft = ifft * window[None, :, None]

    # Overlap and Add
    output_size = (T - 1) * hop_length + win_length
    y = torch.nn.functional.fold(
        ifft, output_size=(1, output_size), kernel_size=(1, win_length), stride=(1, hop_length),
    )[:, 0, 0, pad:-pad]

    # Window envelope
    window_sq = window.square().expand(1, T, -1).transpose(1, 2)
    window_envelope = torch.nn.functional.fold(
        window_sq, output_size=(1, output_size), kernel_size=(1, win_length), stride=(1, hop_length),
    ).squeeze()[pad:-pad]

    # Normalize
    assert (window_envelope > 1e-11).all()
    y = y / window_envelope
    
    return y


def tts(text:str, spk_name:str, temperature:float, length_scale:float, denoise:bool):
    spk_id = speaker_id_dict[spk_name]
    sid = np.array([int(spk_id)]) if spk_id is not None else None
    text_matcha , text_lengths = process_text(0,text,"cpu") 

    # MATCHA VOCOS
    inputs = {
        "x": text_matcha,
        "x_lengths": text_lengths,
        "scales": np.array([temperature, length_scale], dtype=np.float32),
        "spks": sid
    }
    mel_t0 = perf_counter()
    # matcha mel inference
    mel, mel_lengths = model_matcha_mel.run(None, inputs)
    mel_infer_secs = perf_counter() - mel_t0
    print("Matcha Mel inference time", mel_infer_secs)

    vocos_t0 = perf_counter()
    # vocos inference
    wavs_vocos = vocos_inference(mel,denoise)
    vocos_infer_secs = perf_counter() - vocos_t0
    print("Vocos inference time", vocos_infer_secs)

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False, dir="/home/user/app") as fp_matcha_vocos:
        sf.write(fp_matcha_vocos.name, wavs_vocos.squeeze(0), 22050, "PCM_24")

    #MATCHA HIFIGAN

    inputs = {
        "x": text_matcha,
        "x_lengths": text_lengths,
        "scales": np.array([temperature, length_scale], dtype=np.float32),
        "spks": sid
    }
    hifigan_t0 = perf_counter()
    print(f"RTF matcha + vocos { (mel_infer_secs + vocos_infer_secs) / (wavs_vocos.shape[1]/22050) }")
    return fp_matcha_vocos.name

## GUI space

title = """
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
    <div
        style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;"
    > <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
        Natural and efficient TTS in Catalan
    </h1> </div>
</div>
 """

description = """
 
🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis

For vocoders we use [Vocos](https://huggingface.co/BSC-LT/vocos-mel-22khz-cat) trained in a catalan set of ~28 hours.

[Matcha](https://huggingface.co/BSC-LT/matcha-tts-cat-onnx) was trained using openslr69 and festcat datasets

"""

about = """
## 📄 About
Natural and efficient TTS in Catalan: using Matcha-TTS with the Catalan language.

Here you'll be able to find all the information regarding our model, which has been trained with the use of deep learning. If you want specific information on how to train the model you can find it [here](https://huggingface.co/BSC-LT/matcha-tts-cat-multispeaker). The code we've used is also on Github [here](https://github.com/langtech-bsc/Matcha-TTS/tree/dev-cat).

## Table of Contents
<details>
<summary>Click to expand</summary>

- [General Model Description](#general-model-description)
- [Adaptation to Catalan](#adaptation-to-catalan)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [Samples](#samples)
- [Citation](#citation)  
- [Additional Information](#additional-information)

</details>

## General Model Description

**Matcha-TTS** is an encoder-decoder architecture designed for fast acoustic modelling in TTS. 
On the one hand, the encoder part is based on a text encoder and a phoneme duration prediction. Together, they predict averaged acoustic features.
On the other hand, the decoder has essentially a U-Net backbone inspired by [Grad-TTS](https://arxiv.org/pdf/2105.06337.pdf), which is based on the Transformer architecture. 
In the latter, by replacing 2D CNNs by 1D CNNs, a large reduction in memory consumption and fast synthesis is achieved.

**Matcha-TTS** is a non-autorregressive model trained with optimal-transport conditional flow matching (OT-CFM). 
This yields an ODE-based decoder capable of generating high output quality in fewer synthesis steps than models trained using score matching.

## Adaptation to Catalan

The original Matcha-TTS model excels in English, but to bring its capabilities to Catalan, a multi-step process was undertaken. Firstly, we fine-tuned the model from English to Catalan central, which laid the groundwork for understanding the language's nuances. This first fine-tuning was done using two datasets:

 * [Our version of the openslr-slr69 dataset.](https://huggingface.co/datasets/projecte-aina/openslr-slr69-ca-trimmed-denoised) 
 
 * A studio-recorded dataset of central catalan, which will soon be published.
 
 This soon to be published dataset also included recordings of three different dialects:
 
 * Valencian
 
 * Occidental
 
 * Balear 
 
With a male and a female speaker for each dialect.

Then, through fine-tuning for these specific Catalan dialects, the model adapted to regional variations in pronunciation and cadence. This meticulous approach ensures that the model reflects the linguistic richness and cultural diversity within the Catalan-speaking community, offering seamless communication in previously underserved dialects.
 
In addition to training the Matcha-TTS model for Catalan, integrating the eSpeak phonemizer played a crucial role in enhancing the naturalness and accuracy of generated speech. A TTS (Text-to-Speech) system comprises several components, each contributing to the overall quality of synthesized speech. The first component involves text preprocessing, where the input text undergoes normalization and linguistic analysis to identify words, punctuation, and linguistic features. Next, the text is converted into phonemes, the smallest units of sound in a language, through a process called phonemization. This step is where the eSpeak phonemizer shines, as it accurately converts Catalan text into phonetic representations, capturing the subtle nuances of pronunciation specific to Catalan. You can find the espeak version we used [here](https://github.com/projecte-aina/espeak-ng/tree/dev-ca).

After phonemization, the phonemes are passed to the synthesis component, where they are transformed into audible speech. Here, the Matcha-TTS model takes center stage, generating high-quality speech output based on the phonetic input. The model's training, fine-tuning, and adaptation to Catalan ensure that the synthesized speech retains the natural rhythm, intonation, and pronunciation patterns of the language, thereby enhancing the overall user experience.

Finally, the synthesized speech undergoes post-processing, where prosodic features such as pitch, duration, and emphasis are applied to further refine the output and make it sound more natural and expressive. By integrating the eSpeak phonemizer into the TTS pipeline and adapting it for Catalan, alongside training the Matcha-TTS model for the language, we have created a comprehensive and effective system for generating high-quality Catalan speech. This combination of advanced techniques and meticulous attention to linguistic detail is instrumental in bridging language barriers and facilitating communication for Catalan speakers worldwide.

## Intended Uses and Limitations

This model is intended to serve as an acoustic feature generator for multispeaker text-to-speech systems for the Catalan language. 
It has been finetuned using a Catalan phonemizer, therefore if the model is used for other languages it may will not produce intelligible samples after mapping 
its output into a speech waveform. 

The quality of the samples can vary depending on the speaker. 
This may be due to the sensitivity of the model in learning specific frequencies and also due to the quality of samples for each speaker.




## Samples
* Female samples 

<table style="font-size:16px">
  <col width="205">
  <col width="205">
  <td>Valencian</td>
  <td>Occidental</td>
  <td>Balear</td>
<tbody
<table>
  <tbody>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk1/0.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk1/0.wav" type="audio/wav"">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk1/0.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk1/1.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk1/1.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk1/1.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk1/2.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk1/2.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk1/2.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
  </tbody>
</table>

* Male samples:

<table style="font-size:16px">
  <col width="205">
  <col width="205">
<thead>
<tr>
  <td>Valencian</td>
  <td>Occidental</td>
  <td>Balear</td>
</tr>
</thead>
<tbody
<table>
  <tbody>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk0/0.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk0/0.wav" type="audio/wav"">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk0/0.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk0/1.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk0/1.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk0/1.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
    <tr>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/valencia/spk0/2.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/occidental/spk0/2.wav" type="audio/wav">
        </audio>
      </td>
      <td>
        <audio controls="" preload="none" style="width: 200px">
          audio not supported
          <source src="https://github.com/mllopartbsc/assets/raw/c6a393237e712851dd7cc7d10c70dde29d3412ac/matcha_tts_catalan/balear/spk0/2.wav" type="audio/wav">
        </audio>
      </td>
    </tr>
  </tbody>
</table>


## Citation

If this code contributes to your research, please cite the work:

```
@misc{mehta2024matchatts,
      title={Matcha-TTS: A fast TTS architecture with conditional flow matching}, 
      author={Shivam Mehta and Ruibo Tu and Jonas Beskow and Éva Székely and Gustav Eje Henter},
      year={2024},
      eprint={2309.03199},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
```

## Additional Information

### Author
The Language Technologies Unit from Barcelona Supercomputing Center.

### Contact
For further information, please send an email to <langtech@bsc.es>.

### Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.

### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
"""

article = "Training and demo by The Language Technologies Unit from Barcelona Supercomputing Center."

vits2_inference = gr.Interface(
    fn=tts,
    inputs=[
        gr.Textbox(
            value="m'ha costat molt desenvolupar una veu, i ara que la tinc no estaré en silenci.",
            max_lines=1,
            label="Input text",
        ),
        gr.Dropdown(
            choices=speakers,
            label="Speaker id",
            value=DEFAULT_SPEAKER_ID,
            info=f"Models are trained on 47 speakers. You can prompt the model using one of these speaker ids."
        ),
        gr.Slider(
            0.1,
            2.0,
            value=0.667,
            step=0.01,
            label="Temperature",
            info=f"Temperature",
        ),
        gr.Slider(
            0.5,
            2.0,
            value=1.0,
            step=0.01,
            label="Length scale",
            info=f"Controls speech pace, larger values for slower pace and smaller values for faster pace",
        ),
        gr.Checkbox(label="Denoise", info="Removes model bias from vocos", value=True),
    ],
    outputs=[gr.Audio(label="Matcha vocos", interactive=False, type="filepath")]
)

about_article = gr.Markdown(about)

demo = gr.Blocks()

with demo:
    gr.Markdown(title)
    gr.Markdown(description)
    gr.TabbedInterface([vits2_inference, about_article], ["Demo", "About"])
    gr.Markdown(article)

demo.queue(max_size=10)
demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860)