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The code imports the OuteTTS library, configures a text-to-speech model, and defines a function llmSpeech to convert text to speech, which is then exposed to be used outside of this code module.

Run example

npm run import -- "llm voice"

llm voice

import outetts

# Configure the model
model_config = outetts.HFModelConfig_v2(
    model_path="OuteAI/OuteTTS-0.3-1B",
    tokenizer_path="OuteAI/OuteTTS-0.3-1B"
)

globals()["interface"] = None

def llmSpeech(prompt):

  if globals()["interface"] is None:
    # Initialize the interface
    interface = outetts.InterfaceHF(model_version="0.3", cfg=model_config)

    # You can create a speaker profile for voice cloning, which is compatible across all backends.
    # speaker = interface.create_speaker(audio_path="path/to/audio/file.wav")
    # interface.save_speaker(speaker, "speaker.json")
    # speaker = interface.load_speaker("speaker.json")

    # Print available default speakers
    interface.print_default_speakers()
    # Load a default speaker
    speaker = interface.load_default_speaker(name="en_male_1")

    globals()["interface"] = interface
    globals()["speaker"] = speaker
  else:
    interface = globals()["interface"]
    speaker = globals()["speaker"]

  # Generate speech
  gen_cfg = outetts.GenerationConfig(
      text=prompt,
      temperature=0.3,
      repetition_penalty=1.1,
      max_length=4096,
      speaker=speaker,
  )
  output = interface.generate(config=gen_cfg)

  # Save the generated speech to a file
  output.save("output.wav")

__all__ = {
  "llmSpeech": llmSpeech
}

What the code could have been:

import outetts

# Define the model configuration
class ModelConfig:
    """Configuration for the OuteTTS model"""
    def __init__(self, model_path, tokenizer_path):
        self.model_path = model_path
        self.tokenizer_path = tokenizer_path

    def to_dict(self):
        return {
            "model_path": self.model_path,
            "tokenizer_path": self.tokenizer_path
        }

# Define the speaker configuration
class SpeakerConfig:
    """Configuration for the speaker"""
    def __init__(self, name):
        self.name = name

    def to_dict(self):
        return {
            "name": self.name
        }

# Define the interface configuration
class InterfaceConfig:
    """Configuration for the OuteTTS interface"""
    def __init__(self, model_version):
        self.model_version = model_version

    def to_dict(self):
        return {
            "model_version": self.model_version
        }

# Define the generation configuration
class GenerationConfig:
    """Configuration for the speech generation"""
    def __init__(self, text, temperature, repetition_penalty, max_length, speaker):
        self.text = text
        self.temperature = temperature
        self.repetition_penalty = repetition_penalty
        self.max_length = max_length
        self.speaker = speaker

    def to_dict(self):
        return {
            "text": self.text,
            "temperature": self.temperature,
            "repetition_penalty": self.repetition_penalty,
            "max_length": self.max_length,
            "speaker": self.speaker
        }

# Define the OuteTTS interface
class OuteTTS:
    """Implementation of the OuteTTS interface"""
    def __init__(self, model_version, cfg):
        self.model_version = model_version
        self.cfg = cfg
        self.interface = None
        self.speaker = None

    def initialize(self):
        """Initialize the OuteTTS interface"""
        try:
            import torch
            import torchx
        except ImportError:
            print("Error: TTS and TorchX libraries are required")
            return

        try:
            self.interface = outetts.InterfaceHF(model_version=self.model_version, cfg=self.cfg)
        except Exception as e:
            print(f"Error: {e}")
            return

        try:
            # Create a speaker profile for voice cloning
            # self.speaker = self.interface.create_speaker(audio_path="path/to/audio/file.wav")
            # self.interface.save_speaker(speaker, "speaker.json")
            # self.speaker = self.interface.load_speaker("speaker.json")

            # Print available default speakers
            self.interface.print_default_speakers()
            # Load a default speaker
            self.speaker = self.interface.load_default_speaker(name="en_male_1")
        except Exception as e:
            print(f"Error: {e}")

        self.interface = self.interface
        self.speaker = self.speaker

    def generate(self, config):
        """Generate speech using the OuteTTS interface"""
        try:
            output = self.interface.generate(config=config)
        except Exception as e:
            print(f"Error: {e}")
            return

        return output

# Initialize the OuteTTS interface
def llmSpeech(prompt):
    """
    Generate speech using the OuteTTS interface

    Parameters:
    - prompt (str): Text to generate speech from

    Returns:
    - None
    """
    if not hasattr(llmSpeech, 'interface'):
        llmSpeech.interface = OuteTTS(model_version="0.3", cfg=ModelConfig(model_path="OuteAI/OuteTTS-0.3-1B", tokenizer_path="OuteAI/OuteTTS-0.3-1B"))
        llmSpeech.interface.initialize()
    else:
        llmSpeech.interface = llmSpeech.interface

    # Create a speaker profile for voice cloning
    # speaker = llmSpeech.interface.create_speaker(audio_path="path/to/audio/file.wav")
    # llmSpeech.interface.save_speaker(speaker, "speaker.json")
    # speaker = llmSpeech.interface.load_speaker("speaker.json")

    # Print available default speakers
    # llmSpeech.interface.print_default_speakers()
    # Load a default speaker
    speaker = llmSpeech.interface.speaker

    # Define the generation configuration
    config = GenerationConfig(
        text=prompt,
        temperature=0.3,
        repetition_penalty=1.1,
        max_length=4096,
        speaker=speaker
    )

    # Generate speech using the OuteTTS interface
    output = llmSpeech.interface.generate(config=config)

    # Save the generated speech to a file
    output.save("output.wav")

# Expose the llmSpeech function
__all__ = {
    "llmSpeech": llmSpeech
}

Code Breakdown

Importing the OuteTTS Library

import outetts

The code imports the OuteTTS library, which is a text-to-speech (TTS) model.

Configuring the Model

model_config = outetts.HFModelConfig_v2(
    model_path="OuteAI/OuteTTS-0.3-1B",
    tokenizer_path="OuteAI/OuteTTS-0.3-1B"
)

The code configures the model by specifying the paths to the model and tokenizer files.

Defining the llmSpeech Function

def llmSpeech(prompt):

The llmSpeech function takes a prompt parameter, which is the text to be converted into speech.

Initializing the Interface

if globals()["interface"] is None:
    interface = outetts.InterfaceHF(model_version="0.3", cfg=model_config)
    #... (other interface initialization code)

The code checks if the interface variable is None. If it is, the interface is initialized with the specified model version and configuration.

Loading a Default Speaker

# Load a default speaker
speaker = interface.load_default_speaker(name="en_male_1")

The code loads a default speaker with the name "en_male_1".

Generating Speech

gen_cfg = outetts.GenerationConfig(
    text=prompt,
    temperature=0.3,
    repetition_penalty=1.1,
    max_length=4096,
    speaker=speaker,
)
output = interface.generate(config=gen_cfg)

The code generates speech from the prompt text using the loaded speaker and configuration.

Saving the Generated Speech

output.save("output.wav")

The generated speech is saved to a file named "output.wav".

Exposing the llmSpeech Function

__all__ = {
  "llmSpeech": llmSpeech
}

The llmSpeech function is exposed to be used outside of this code module.