Neural Codec Language Models Explained with Code

A neural codec language model (NCLM) is a type of machine learning model that is designed to perform both encoding and decoding tasks for natural language data. NCLMs consist of two main components: an encoder and a decoder.

The encoder takes in a input sequence, typically a sequence of words, and converts it into a fixed-length representation, called a “code”. This code captures the meaning of the input sequence in a compact form that is suitable for use by the decoder.

The decoder then takes the code produced by the encoder and converts it back into an output sequence, which is typically a sequence of words. The output sequence should be similar to the original input sequence in terms of meaning and content.

The NCLM is trained by giving it pairs of input-output sequences and adjusting the model’s parameters to minimize the difference between the output sequences produced by the decoder and the target sequences.

In essence, the neural codec models are a specific type of the family of autoencoder models. The goal is to learn an efficient representation of the input that can be used for many purposes like language translation, summarization, text generation, and others.

How is a NCLM used for TTS?

A neural codec language model (NCLM) can be used for text-to-speech (TTS) by training the model to learn the mapping between text and speech. This is typically done by training the NCLM on a dataset of paired text and speech data.

The encoder component of the NCLM is trained to convert text into a fixed-length representation, or code, that captures the meaning of the text. The decoder component is then trained to convert this code back into speech.

Once the model is trained, it can be used to generate speech from new text inputs by encoding the text into a code, and then decoding the code into speech. The speech generated by the model will be a synthetic version of the input text.

A typical TTS pipeline would involve the following steps:

1. The input text is passed through an input pre-processing module to clean and tokenize it.
2. The preprocessed text is then passed through the encoder component of the NCLM to produce a code.
3. This code is passed through the decoder component of the NCLM to produce the output speech.
4. This output speech can then be passed through post-processing steps such as audio synthesis to produce a final audio file.

By using the NCLM, TTS systems can generate high-quality speech that is natural-sounding and similar to the input text in terms of meaning and content.

How are NCLM based models different from traditional TTS systems?

Neural codec language model (NCLM) based models are different from traditional text-to-speech (TTS) systems in several key ways.

1. NCLM-based models are end-to-end systems: Unlike traditional TTS systems, which typically consist of multiple stages such as text analysis, prosody prediction, and speech synthesis, NCLM-based models are end-to-end systems that can be trained to perform all of these tasks simultaneously. This makes them more efficient and easier to use, as well as allowing to produce more natural outputs.
2. NCLM-based models are data-driven: Traditional TTS systems rely heavily on hand-crafted rules and expert knowledge to model speech synthesis, while NCLM-based models are data-driven and can learn to model the relationship between text and speech directly from data. This allows them to learn more complex and nuanced patterns in the data and produce more natural-sounding speech.
3. NCLM-based models can handle more complex and unstructured input data: Traditional TTS systems are usually designed to work with clean, well-formed, and structured text inputs. NCLM-based models, on the other hand, can handle more complex and unstructured input data, such as social media posts or speech with different accents or languages. This makes them more flexible and able to produce high-quality speech from a wider range of input data.
4. NCLM-based models can be fine-tuned for specific tasks or speakers: NCLM-based models can be fine-tuned to specific tasks, such as TTS for a particular accent, or speaker, by training them on data from that specific task or speaker. This enables them to generate speech that is highly natural and similar to the specific task or speaker.

Here is sample code for training an NCLM for text-to-speech (TTS) using the Python programming language and the Keras library:

from keras.layers import Input, LSTM, Dense
from keras.models import Model

# Define the input and output shapes
input_shape = (None, num_features)
output_shape = (None, num_features)

# Define the encoder and decoder
encoder_inputs = Input(shape=input_shape)
encoder = LSTM(latent_dim)(encoder_inputs)

decoder_inputs = Input(shape=(None, num_features))
decoder_lstm = LSTM(latent_dim, return_sequences=True)
decoder_outputs = decoder_lstm(decoder_inputs, initial_state=encoder)

# Define the NCLM model
nclm = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Compile the NCLM model
nclm.compile(optimizer='adam', loss='mean_squared_error')

# train NCLM model
nclm.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)