MetaVoice TTS with Voice Cloning Guide

​

Introduction

Text-to-speech (TTS) technology has seen remarkable advancements in recent years, becoming increasingly accessible and efficient. Contemporary TTS models utilize deep learning and artificial intelligence to generate speech that is both natural-sounding and highly accurate. These advancements have led to widespread applications in various real-life scenarios, including voice assistants, audiobook narration, and accessibility tools for individuals with visual impairments or reading difficulties. In this article, we will explore the capabilities of one such TTS model, MetaVoice, and demonstrate how to leverage its features on SaladCloud in a cloud-based environment.

If you are looking for fast deployment of MetaVoice Endpoint on SaladCloud move to Deploying MetaVoice Endpoint to Salad

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Discover MetaVoice: The Open-Source Voice Cloning Tool

MetaVoice-1B is a sophisticated text-to-speech (TTS) model with an impressive 1.2 billion parameters, trained on an extensive dataset of 100,000 hours of speech. It is specifically tailored to produce emotionally rich English speech rhythms and tones, setting it apart for its accuracy and lifelike voice synthesis.

A notable feature of MetaVoice-1B is its capability for zero-shot voice cloning, which can accurately replicate American and British voices with just a 30-second audio sample. Additionally, it boasts cross-lingual cloning abilities, demonstrated with minimal training data, such as one minute for Indian accents. Released under the flexible Apache 2.0 license, MetaVoice-1B is particularly well-suited for long-form synthesis, making it a versatile tool for various applications.

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Exploring MetaVoice Architecture

MetaVoice employs a sophisticated architecture to convert text into natural-sounding speech. Here’s a breakdown of the process:

1. Token Prediction The model predicts EnCodec tokens based on input text and speaker information. These tokens are then diffused up to the waveform level, with post-processing applied to enhance audio quality.
2. Token Generation A causal GPT model is used to predict the first two hierarchies of EnCodec tokens. Both text and audio are included in the LLM context, while speaker information is incorporated through conditioning at the token embedding layer. This speaker conditioning is derived from a separate speaker verification network.
3. Flattened Interleaved Prediction The two hierarchies are predicted in a “flattened interleaved” manner. This means the model predicts the first token of the first hierarchy, then the first token of the second hierarchy, followed by the second token of the first hierarchy, and so on.
4. Condition-Free Sampling To enhance the model’s cloning capability, condition-free sampling is employed.
5. Tokenization The text is tokenized using a custom-trained BPE tokenizer with 512 tokens. Notably, the model skips predicting semantic tokens, as this was found to be unnecessary for effective synthesis.
6. Non-Causal Transformer A non-causal (encoder-style) transformer is used to predict the remaining six hierarchies from the first two. This smaller model (~10 million parameters) demonstrates extensive zero-shot generalization to most speakers tested. Being non-causal, it can predict all timesteps in parallel.
7. Multi-Band Diffusion Waveforms are generated from EnCodec tokens using multi-band diffusion. This approach results in clearer speech compared to traditional methods, although it can introduce background artifacts.
8. Artifact Removal DeepFilterNet is utilized to clean up artifacts introduced by multi-band diffusion, ensuring the final output is clear and pleasant to the ear.

This architecture allows MetaVoice to produce high-quality, natural-sounding speech with effective speaker cloning and generalization capabilities.

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Handling Text Length Limitations in MetaVoice

During our evaluation of MetaVoice, we encountered limitations regarding the maximum length of text the model could process effectively in one go. Although the default token limit is set to 2048 tokens per batch, we observed that the model’s performance began to degrade with even smaller numbers of tokens.

To address this issue, we implemented a preprocessing step to divide the text into smaller segments. Specifically, we found that breaking the text into two-sentence pieces allowed us to stay within the model’s processing capabilities without compromising the quality of the generated speech.

For sentence tokenization, we utilized the Punkt Sentence Tokenizer, which is a part of the Natural Language Toolkit (NLTK). This tokenizer is effective in identifying sentence boundaries, making it a suitable choice for segmenting our text data into manageable pieces for MetaVoice processing.

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GPU Specifications and Selection for MetaVoice

The official documentation for MetaVoice suggests the use of GPUs with a minimum of 12GB of VRAM to ensure optimal performance. However, during our trials, we explored the use of GPUs with lower VRAM and found that they could still deliver satisfactory results. This necessitated a meticulous selection process from SaladCloud’s GPU fleet to identify compatible options that could handle the processing demands of MetaVoice.

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Project Overview: TTS with Voice Cloning using MetaVoice and SaladCloud

In this project, we aim to deploy a flexible voice solution that enables text-to-speech conversion with addition of a narrator’s voice tone. This solution will be accessible as an API.

Workflow:

1. Request: The process initiates with an API request.
2. Input Data: Text files and reference voices are stored on Azure.
3. TTS and Voice Cloning: The text file is processed, and an audio file is generated based on the input voice, following MetaVoice’s architecture.
4. Storage and Accessibility: The generated audio file is uploaded back to Azure for easy access and further usage.

Through this project, we aim to demonstrate that advanced voice cloning and text-to-speech synthesis are not only reserved for large organizations with extensive resources. By leveraging MetaVoice and SaladCloud , we make cutting-edge voice technology accessible to a wider audience, enabling the creation of realistic and customizable speech with minimal effort. This initiative showcases how cloud computing and AI models can work together to address real-world applications in voice synthesis and cloning, offering value in various scenarios such as content creation, accessibility, and personalized communication.

For average processing prices, refer to our benchmarks: MetaVoice AI Text-to-Speech (TTS) Benchmark: Narrate 100,000 words for only $4.29 on Salad

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Reference Architecture

• Deployment:
  • The FastAPI application is containerized using Docker, providing a consistent and isolated environment for deployment.
  • The Docker container is then deployed on SaladCloud’s compute resources to leverage their processing capabilities.
  • The Docker image is stored in the SaladCloud Docker Container Registry, ensuring secure and easy access for deployment and updates.

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Folder Structure

Our full solution is stored here: MetaVoice Git Repo

openvoice-on-salad/
├─ src/
│  ├─ infrastructure/
│  │  ├─ main.bicep (azure resources deployment)
│  ├─ python/
│  │  ├─ api /
│  │  │  ├─ inference/
│  │  │  │  ├─ dev/
│  │  │  │  │  ├─ setup
│  │  │  │  ├─ fast.py
│  │  │  │  ├─ setup.py
│  │  │  │  ├─ assets
│  │  │  │  ├─ fam
│  │  │  │  ├─ outputs
│  │  │  ├─ .dockerignore
│  │  │  ├─ Dockerfile

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Local Development Setup and Testing

For a smooth customization process, we have made our GitHub repository public. Begin by setting up an efficient local development environment. Execute the setup script to install all dependencies and download the MetaVoice checkpoints. This script ensures that the dependencies function correctly during development. The complete contents of the setup script are provided below.

The Setup Script:

set -e
echo "setup the curent environment"
CURRENT_DIRECTORY="$( dirname "${BASH_SOURCE[0]}" )"
cd "${CURRENT_DIRECTORY}"
echo "current directory: $( pwd )"
echo "setup development environment"
METAVOICE="$( cd .. && pwd )"
echo "dev directory set to: ${METAVOICE}"
echo "remove old virtual environment"
rm -rf "${METAVOICE}/.venv"
echo "create new virtual environment"
python3.10 -m venv "${METAVOICE}/.venv"
echo "activate virtual environment"
source "${METAVOICE}/.venv/bin/activate"
cd "${METAVOICE}"
# install ffmpeg
echo "installing ffmpeg"
wget https://johnvansickle.com/ffmpeg/builds/ffmpeg-git-amd64-static.tar.xz
wget https://johnvansickle.com/ffmpeg/builds/ffmpeg-git-amd64-static.tar.xz.md5
md5sum -c ffmpeg-git-amd64-static.tar.xz.md5
tar xvf ffmpeg-git-amd64-static.tar.xz
sudo mv ffmpeg-git-*-static/ffprobe ffmpeg-git-*-static/ffmpeg /usr/local/bin/
rm -rf ffmpeg-git-*

# install rust if not installed (ensure you've restarted your terminal after installation)
echo "installing rust"
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env
echo "installing dependencies ..."
(cd "${METAVOICE}" && pip install --upgrade pip && pip install -r requirements.txt)
pip install --upgrade torch torchaudio
pip install -e .

To establish a clean virtual environment and install all the necessary libraries, you simply needs to execute the script using this command:

bash dev/setup

This script will prepare your local development environment for working with MetaVoice, ensuring that you have all the required tools and dependencies to start customizing and testing the application.

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Voice cloning test with MetaVoice on SaladCloud

To explore the capabilities of MetaVoice, we followed the instructions provided by MetaVoice and used their source code as our base: MetaVoice repo. Together with all the processing scripts MetaVoice also provides a ready to go fast api that we slightly updated to fit our needs. Here is the base MataVoice Fast API:

import json
import logging
import shlex
import subprocess
import tempfile
import warnings
from pathlib import Path
from typing import Literal, Optional

import fastapi
import fastapi.middleware.cors
import tyro
import uvicorn
from attr import dataclass
from fastapi import Request
from fastapi.responses import Response

from fam.llm.fast_inference import TTS
from fam.llm.utils import check_audio_file

logger = logging.getLogger(__name__)


## Setup FastAPI server.
app = fastapi.FastAPI()


@dataclass
class ServingConfig:
    huggingface_repo_id: str = "metavoiceio/metavoice-1B-v0.1"
    """Absolute path to the model directory."""

    temperature: float = 1.0
    """Temperature for sampling applied to both models."""

    seed: int = 1337
    """Random seed for sampling."""

    port: int = 58003

    quantisation_mode: Optional[Literal["int4", "int8"]] = None


# Singleton
class _GlobalState:
    config: ServingConfig
    tts: TTS


GlobalState = _GlobalState()


@dataclass(frozen=True)
class TTSRequest:
    text: str
    speaker_ref_path: Optional[str] = None
    guidance: float = 3.0
    top_p: float = 0.95
    top_k: Optional[int] = None


@app.get("/health")
async def health_check():
    return {"status": "ok"}


@app.post("/tts", response_class=Response)
async def text_to_speech(req: Request):
    audiodata = await req.body()
    payload = None
    wav_out_path = None

    try:
        headers = req.headers
        payload = headers["X-Payload"]
        payload = json.loads(payload)
        tts_req = TTSRequest(**payload)
        with tempfile.NamedTemporaryFile(suffix=".wav") as wav_tmp:
            if tts_req.speaker_ref_path is None:
                wav_path = _convert_audiodata_to_wav_path(audiodata, wav_tmp)
                check_audio_file(wav_path)
            else:
                # TODO: fix
                wav_path = tts_req.speaker_ref_path

            if wav_path is None:
                warnings.warn("Running without speaker reference")
                assert tts_req.guidance is None

            wav_out_path = GlobalState.tts.synthesise(
                text=tts_req.text,
                spk_ref_path=wav_path,
                top_p=tts_req.top_p,
                guidance_scale=tts_req.guidance,
            )

        with open(wav_out_path, "rb") as f:
            return Response(content=f.read(), media_type="audio/wav")
    except Exception as e:
        # traceback_str = "".join(traceback.format_tb(e.__traceback__))
        logger.exception(f"Error processing request {payload}")
        return Response(
            content="Something went wrong. Please try again in a few mins or contact us on Discord",
            status_code=500,
        )
    finally:
        if wav_out_path is not None:
            Path(wav_out_path).unlink(missing_ok=True)


def _convert_audiodata_to_wav_path(audiodata, wav_tmp):
    with tempfile.NamedTemporaryFile() as unknown_format_tmp:
        if unknown_format_tmp.write(audiodata) == 0:
            return None
        unknown_format_tmp.flush()

        subprocess.check_output(
            # arbitrary 2 minute cutoff
            shlex.split(f"ffmpeg -t 120 -y -i {unknown_format_tmp.name} -f wav {wav_tmp.name}")
        )

        return wav_tmp.name


if __name__ == "__main__":
    for name in logging.root.manager.loggerDict:
        logger = logging.getLogger(name)
        logger.setLevel(logging.INFO)
    logging.root.setLevel(logging.INFO)

    GlobalState.config = tyro.cli(ServingConfig)
    GlobalState.tts = TTS(seed=GlobalState.config.seed, quantisation_mode=GlobalState.config.quantisation_mode)

    app.add_middleware(
        fastapi.middleware.cors.CORSMiddleware,
        allow_origins=["*", f"http://localhost:{GlobalState.config.port}", "http://localhost:3000"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )
    uvicorn.run(
        app,
        host="0.0.0.0",
        port=GlobalState.config.port,
        log_level="info",
    )

​

Additional Endpoints

Due to the computational needs of MetaVoice, which requires approximately one second per word to convert text to audio, we’ve introduced two new endpoints to accommodate different text lengths:

/process_short_text: This endpoint is designed for processing shorter texts where the response time is not a significant concern. It directly calls the inference function and waits for the processing to complete before returning the result. This synchronous approach is suitable for texts that can be processed relatively quickly.

@app.post("/process_short_text")
async def process(
    connection_string: str = Query("DefaultEndpointsProtocol=https;AccountName=accountname;AccountKey=key;EndpointSuffix=core.windows.net", description="Azure Storage Connection String"),
    input_container_name: str = Query("requests", description="Container name for input files"),
    output_container_name: str = Query("results", description="Container name for output files"),
    voices_container_name: str = Query("voices", description="Container name for voice files"),
    reference_voice: str = Query(None, description="Voice file to be used as reference"),
    text_file: str = Query(description="Text file to be used for TTS"),
):
    result = inference(connection_string, input_container_name, output_container_name, voices_container_name, reference_voice, text_file)
    return result

/process_long_text: This endpoint is tailored for processing longer texts where immediate response is not feasible due to the extended processing time and a big chance of requests timeouts. It leverages FastAPI’s BackgroundTasks to execute the inference function asynchronously. This means that the endpoint will initiate the processing task in the background and immediately return a response indicating that the process has started. The actual processing will continue independently, and the results will be stored in Azure Storage once completed.

@app.post("/process_long_text")
async def process_long(
    background_tasks: BackgroundTasks,
    connection_string: str = Query("DefaultEndpointsProtocol=https;AccountName=accountname;AccountKey=key;EndpointSuffix=core.windows.net", description="Azure Storage Connection String"),
    input_container_name: str = Query("requests", description="Container name for input files"),
    output_container_name: str = Query("results", description="Container name for output files"),
    voices_container_name: str = Query("voices", description="Container name for voice files"),
    reference_voice: str = Query(None, description="Voice file to be used as reference"),
    text_file: str = Query(description="Text file to be used for TTS"),
):
    background_tasks.add_task(inference, connection_string, input_container_name, output_container_name, voices_container_name, reference_voice, text_file)
    return {"status": "Process started"}

By distinguishing between short and long texts, these endpoints offer a more efficient way to handle text-to-speech and voice cloning tasks with MetaVoice, ensuring that users get timely request responses.

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Request Arguments

Here’s an explanation of each argument:

connection_string: The Azure Storage connection string, which is used to authenticate and connect to your Azure Storage account. It typically includes the account name, account key, and endpoint suffix.

input_container_name: The name of the Azure Blob Storage container where the input text files are stored. The API will fetch the text file from this container for processing.

output_container_name: The name of the Azure Blob Storage container where the resulting audio files will be stored. The final voice audio file will be uploaded to this container.

voices_container_name: The name of the Azure Blob Storage container where the reference voice files are stored. The API will fetch the reference voice file from this container.

reference_voice: The name of the reference voice file to be used for voice cloning. The API will attempt to clone the voice from this file.

text_file: The name of the text file containing the text to be synthesized into speech. The API will read the text from this file and process it using the MetaVoice model.

When a request is made to this endpoint with the necessary parameters, the API will fetch the text and reference voice files from Azure Storage, perform voice cloning and text-to-speech synthesis, and upload the resulting audio file back to Azure Storage. Live response will include the status of the process and the location of the resulting audio file.

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Data Preprocessing. Handling Large Text Inputs.

To process larger text inputs with MetaVoice, we need to divide the text into smaller chunks. We chose to split the text into segments of two sentences each for manageable processing. For this task, we employed the Punkt Sentence Tokenizer from the Natural Language Toolkit (NLTK). Once the audio for each chunk is processed, we then need to concatenate the audio files to create the final output.

Here’s a code snippet that demonstrates how to split the text into sentences and combine the resulting audio files:

import nltk
import wave

def split_into_sentences(text):
    nltk.download('punkt')  # Download the Punkt tokenizer.
    return nltk.tokenize.sent_tokenize(text)

def combine_wav_files(input_files, output_file):
    data = []

    for file in input_files:
        with wave.open(file, 'rb') as wav_file:
            data.append([wav_file.getparams(), wav_file.readframes(wav_file.getnframes())])

    output_params = data[0][0]

    with wave.open(output_file, 'wb') as output_wav_file:
        output_wav_file.setparams(output_params)
        for params, frames in data:
            output_wav_file.writeframes(frames)

In this code:

split_into_sentences(text) uses the Punkt Sentence Tokenizer to split the input text into individual sentences.

combine_wav_files(input_files, output_file) combines the audio data from a list of input WAV files into a single output WAV file.

By incorporating these functions into our processing pipeline, we can handle larger text inputs efficiently and produce a seamless final audio output.

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Integrating Azure Storage and Organizing Local Folders

To manage input and output files effectively, we integrate Azure Storage into our solution and create local directories to store temporary data during processing. You can use any other storage provider you prefer.

Here’s how we set up the Azure Storage connection and organize the local folders:

from azure.storage.blob import ContainerClient
import os

# Create local paths
output_dir = '.data/outputs'
voice_dir = '.data/input_voice'
text_dir = '.data/input_text'

# Create directories if they don't exist
os.makedirs(output_dir, exist_ok=True)
os.makedirs(voice_dir, exist_ok=True)
os.makedirs(text_dir, exist_ok=True)
os.makedirs('.data/tmp', exist_ok=True)

def azure_initiate(result_blob: str, storage_connection_string: str):
    """
    Initialize a connection to an Azure Storage container.
    """
    azure_client = ContainerClient.from_connection_string(
        storage_connection_string, result_blob
    )
    return azure_client

def retrieve_file(container_client, file_name):
    """
    Retrieve a file from an Azure Storage container.
    """
    return container_client.get_blob_client(file_name)

We create local directories for storing output audio files, input voice samples, input text files, and temporary data during processing.

The azure_initiate function establishes a connection to an Azure Storage container using the provided connection string and container name.

The retrieve_file function retrieves a specific file from the Azure Storage container, which is useful for fetching input text and voice files for processing.

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Processing Logic

The inference function orchestrates the entire process of fetching input data from Azure Storage, performing text-to-speech synthesis and voice cloning, and uploading the resulting audio file back to Azure Storage. Here’s how it works:

def inference(connection_string: str, input_container_name: str, output_container_name: str, voices_container_name: str, reference_voice: str,  text_file: str = None):
    # authenticate in azure

    input_blob = azure_initiate(input_container_name, connection_string)
    result_blob = azure_initiate(output_container_name, connection_string)
    voices_blob = azure_initiate(voices_container_name, connection_string)

    process_start_time = datetime.datetime.now()

    # start processing

    # Load the data (voice reference and text)
    if text_file not in os.listdir(text_dir):
        # download blob with name text_file from input_container_name
        text = retrieve_file(input_blob, text_file).download_blob().readall()
        with open(f"{text_dir}/{text_file}", "wb") as f:
            f.write(text)
    # read the text file
    with open(f"{text_dir}/{text_file}", "r") as f:
        text = f.read()
    # download reference voice
    voice_local_path = f'{voice_dir}/{reference_voice}'
    if reference_voice not in os.listdir(voice_dir):
        voice = retrieve_file(voices_blob, reference_voice).download_blob()
        with open(f"{voice_local_path}", "wb") as f:
            voice.readinto(f)

    # TTS process:

    tts_req = TTSRequest(text=text, speaker_ref_path=voice_local_path)
    with tempfile.NamedTemporaryFile(suffix=".wav") as wav_tmp:
        if tts_req.speaker_ref_path is None:
            wav_path = "./assets/bria.mp3"
        else:
            # TODO: fix
            wav_path = tts_req.speaker_ref_path

        if wav_path is None:
            warnings.warn("Running without speaker reference")
            assert tts_req.guidance is None
        if len(tts_req.text.split()) > 10:
            sentences = split_into_sentences(tts_req.text)
            list_of_wav_out = []
            for sentence in sentences:
                wav_out_path = GlobalState.tts.synthesise(
                    text=sentence,
                    spk_ref_path=wav_path,
                    top_p=tts_req.top_p,
                    guidance_scale=tts_req.guidance,
                )
                list_of_wav_out.append(wav_out_path)
            wav_out_path = "." + text_file.split(".")[1] + "_" + reference_voice.split(".")[0] + "_meta" + ".wav"
            combine_wav_files(list_of_wav_out, wav_out_path)
        else:
            wav_out_path = GlobalState.tts.synthesise(
                text=tts_req.text,
                spk_ref_path=wav_path,
                top_p=tts_req.top_p,
                guidance_scale=tts_req.guidance,
            )
    # save result to azure
    end_time = datetime.datetime.now()
    result_file_name = text_file.split(".")[0] + "_" + reference_voice.split(".")[0] + "_meta" + ".wav"
    output_blob_client = result_blob.get_blob_client(result_file_name)
    # upload wav file from local path to blob
    with open(wav_out_path, "rb") as bytes_data:
        output_blob_client.upload_blob(bytes_data, overwrite=True)

    return {"status": "success", "result saved to": f"{output_container_name}/{result_file_name}", "processing time": str(end_time - process_start_time)}

We authenticate with Azure Storage and fetch the input text and reference voice files. The text is split into sentences using the split_into_sentences function to manage the length limitations of MetaVoice. Each sentence is processed individually, and the resulting audio files are combined into a final output file using the combine_wav_files function. The final audio file is uploaded back to Azure Storage, and the function returns the status, location of the saved file, and processing time.

Local Testing with Uvicorn:

MetaVoice provides us with a ready-to-use FastAPI script, so we don’t need to create one from scratch. Before deploying our FastAPI application to the cloud, it’s crucial to test it locally to ensure everything is functioning as expected. For this purpose, we use Uvicorn, a lightning-fast ASGI server implementation that’s ideal for running FastAPI applications. Uvicorn not only serves as a local development server but also plays a key role in running our application in a cloud environment.

If you haven’t already installed Uvicorn, you can do so using the following command:

pip install uvicorn

If you used our setup script to install all the dependencies, Uvicorn should already be installed.

To start the FastAPI application locally with Uvicorn, run the following command in your terminal:

uvicorn main:app --host 0.0.0.0 --port 8000

After running the command, you should see output in your terminal indicating that Uvicorn is running and serving your FastAPI application.

You can then access the interactive API documentation at http://localhost:8000/docs to test your endpoints.

By testing locally with Uvicorn, we can ensure our FastAPI application is ready for deployment and can smoothly transition to a cloud environment.

​

Containerizing the FastAPI Application with Docker

After thoroughly testing our FastAPI application, the next step is to containerize it using Docker. This ensures that our application can be deployed reliably in the cloud. MetaVoice provides a Dockerfile that uses the nvidia/cuda:12.1.0-devel-ubuntu22.04 base image, which is compatible with MetaVoice’s requirements.

The Dockerfile sets up the necessary environment variables for NVIDIA compatibility, installs essential packages, and sets the working directory to /app. It also copies the application code into the container and installs specific versions of PyTorch, torchaudio, and other required Python packages. Additionally, it downloads the AzCopy tool for efficient data transfer to and from Azure storage.

Here is the Dockerfile:

FROM nvidia/cuda:11.7.1-devel-ubuntu22.04 as cuda-base
# Set some environment variable for better NVIDIA compatibility
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

ENV DEBIAN_FRONTEND=noninteractive

# Set the working directory to /app
WORKDIR /app
# Copy the inference folder to /app/inference
COPY /inference /app/inference

# Install curl and add the NodeSource repositories
RUN apt-get update && apt-get install -y software-properties-common
RUN add-apt-repository ppa:deadsnakes/ppa
RUN apt-get update && apt-get install -y python3.9
RUN apt-get update && apt-get install -y curl wget ffmpeg unzip git python3-pip
# Update pip and install requirements
RUN pip install --upgrade pip
RUN pip install torch==1.13.1+cu117 torchvision>=0.13.1+cu117 torchaudio>=0.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 --no-cache-dir
RUN pip install  -r inference/requirements.txt
RUN pip install uvicorn

# Download AzCopy
RUN wget -O azcopy.tar.gz https://aka.ms/downloadazcopy-v10-linux && tar -xf azcopy.tar.gz --strip-components=1

# Move AzCopy to the /usr/bin directory
RUN mv azcopy /usr/bin/

WORKDIR /app/inference
# Download the cloning model
RUN wget https://myshell-public-repo-hosting.s3.amazonaws.com/checkpoints_1226.zip && \
    unzip -o checkpoints_1226.zip && \
    rm -r checkpoints_1226.zip

CMD ["uvicorn", "fast:app", "--host", "::", "--port", "80"]

By following this Dockerfile, our FastAPI application is prepared for deployment to a cloud environment with Docker support, ensuring consistent performance and compatibility. We use Docker container registry to store the image, but you can use any container registry you prefer.

​

Deploying Solution to Salad

We’ve reached the final and most exciting stage of our project: deploying our solution to SaladCloud. If you’re not making any additional customizations, you can directly proceed to this step.

Deploying your containerized FastAPI application to SaladCloud’s GPU Cloud is a very efficient and cost-effective way to run your text-to-speech solutions. Here’s how to deploy the solution using the SaladCloud portal:

1. Create an Account: Sign up for an account on SaladCloud’s Portal if you haven’t already.
2. Create an Organization: Once logged in, set up your organization within the SaladCloud platform to manage your deployments and resources.
3. Deploy Container Group: Go to the “Container Groups” section in the SaladCloud portal and select “Deploy a Container Group” to begin deploying your FastAPI application to SaladCloud’s infrastructure.

We now need to set up all of our container group parameters:

Configure Container Group:

1. Create a unique name for your Container group
2. Pick the Image Source: In our case we are using a public SaladCloud registry. Click Edit next to Image source. Under image name paste the image path: saladtechnologies/metavoice-api:1.0.0 If you are using your custom solution, specify your image location.

1. Replica count: It is recommended to use 3 or more replicas for production. We will use just 1 for testing.
2. Pick compute resources: Pick how much cpu, ram and gpu you want to allocate to your process. MetaVoice documentation specifies that the models needs at least 12GB GPU RAM. Checkout our benchmark to see what GPU version best suites your needs.
3. Networking. Click “Edit“ next to it, check “Enable Networking“ and set port to 80:

1. Optional Settings: SaladCloud gives you some great options like health check probe, external logging and passing environment variables.
2. Update Command We have not updated MetaVoice’s Entrypoint in the Dockerfile, so we will need to override it under “Command”. We need to change our command to uvicorn fast:app and add a few arguments: —host :: —port 80. This will make sure our endpoint is accessible with IPv6 and port 80:

Additionally, for enhanced security, you have the option to enable Authentication under networking. When activated, you’ll need to include your personal token with each API call. You can locate your token here: https://portal.salad.com/api-key

With all configurations complete, deploying your FastAPI application on SaladCloud is a straightforward process. Leveraging SaladCloud’s platform ensures that your text-to-speech API operates on a robust infrastructure capable of handling demanding tasks cost-effectively.

Finally, ensure the “AutoStart container group once the image is pulled” option is checked, then click “Deploy”. With that, we’re ready to go. Let’s wait for our solution to deploy and then proceed with testing.

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Advantages of Selecting Salad:

• Cost-Efficiency: SaladCloud’s GPU cloud services are priced favorably when compared to other cloud providers, allowing you to leverage additional resources for your application while minimizing expenses.
• Intuitive Platform: SaladCloud emphasizes a seamless user experience, providing an easy-to-navigate interface that streamlines the deployment and management of applications in the cloud.
• Robust Documentation and Support: SaladCloud furnishes detailed documentation to facilitate deployment, configuration, and problem-solving, supported by a committed team available to provide assistance as needed.

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Test Full Solution deployed to Salad

Once your solution is deployed on Salad, the next step is to interact with your FastAPI application using its public endpoint. SaladCloud provides you with a deployment URL, which allows you to send requests to your API using SaladCloud’s infrastructure, just as you would locally.

You can use this URL to access your FastAPI application’s Swagger page, which is now hosted in the cloud. Replace localhost in your local URL with the provided deployment URL to access the Swagger page. For example: https://tomato-cayenne-zjomiph125nsc021.salad.cloud/docs

You will see your Swagger page similar to this:

On the Swagger page, you have the ability to engage with your API by supplying the necessary parameters to initiate the process. Certain parameters are optional, and it may not be necessary to modify them if you’re utilizing the default Azure container names. It’s important to mention that this solution relies on Azure storage, so ensure that your Azure resources are set up beforehand. If you’re considering using a different storage provider, refer to the comprehensive solution documentation for guidance. The complete list of arguments has been provided earlier in the document.

You can now use your endpoint with Swagger to interact with your API directly through the browser. Alternatively, you can send requests to your endpoint using tools like curl or Postman for testing and integration into your applications. Enjoy leveraging the power of MetaVoice and SaladCloud for your text-to-speech and voice cloning needs!