# Implementing Custom Voice Cloning with Pocket TTS in the Speech-to-Speech Pipeline

> Implement custom voice cloning with Pocket TTS in the speech-to-speech pipeline. Generate speaker embeddings and use VoicePrompt with PocketTTSHandler to synthesize audio in your target voice.

- Repository: [Hugging Face/speech-to-speech](https://github.com/huggingface/speech-to-speech)
- Tags: how-to-guide
- Published: 2026-08-09

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**You can implement custom voice cloning in the huggingface/speech-to-speech library by generating speaker embeddings and passing them through the `VoicePrompt` class to the `PocketTTSHandler`, which processes these embeddings via Pocket TTS's API to synthesize audio in the target voice.**

The huggingface/speech-to-speech repository provides a modular pipeline architecture that connects speech-to-text (STT) front-ends, language model (LM) back-ends, and text-to-speech (TTS) synthesizers. Implementing custom voice cloning with Pocket TTS leverages the `PocketTTSArguments` dataclass and the `VoicePrompt` message structure to inject personalized speaker characteristics into the audio generation flow without modifying the core pipeline logic.

## Architecture Overview

The speech-to-speech pipeline maintains strict separation of concerns through handler-based architecture. The system routes audio through distinct processing stages, with the TTS component consuming text output and optional voice embeddings to produce cloned speech.

- **Pipeline Core** – Orchestrates the flow of audio → transcription → LM → synthesized voice via [`src/speech_to_speech/s2s_pipeline.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/s2s_pipeline.py). This module manages `Control` and `CancelScope` utilities for interrupting or replacing voice segments on the fly.

- **Handler Base** – Provides the abstract base class in [`src/speech_to_speech/baseHandler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/baseHandler.py) that enforces a unified `handle` API across all STT, TTS, and language model handlers.

- **Pocket TTS Handler** – Implements voice synthesis in [`src/speech_to_speech/TTS/pocket_tts_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/TTS/pocket_tts_handler.py). The `handle` method converts LM-generated text into audio samples by communicating with Pocket TTS's REST API or local model loader.

- **Argument Class** – Stores configuration options including model path, voice ID, and embedding dimensions in [`src/speech_to_speech/arguments_classes/pocket_tts_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/pocket_tts_arguments.py).

- **Voice Prompt** – Encapsulates target voice characteristics through the `VoicePrompt` class defined in [`src/speech_to_speech/pipeline/messages.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/pipeline/messages.py), which transports custom voice fingerprints through the pipeline.

## How Custom Voice Cloning Works

The cloning mechanism operates through three distinct phases that keep voice generation logic decoupled from the pipeline core.

**1. Generate a Speaker Embedding**

Use any voice-cloning model or encoder to produce a fixed-size embedding tensor from a reference audio clip. This embedding captures the acoustic characteristics of the target speaker.

**2. Pass the Embedding to Pocket TTS**

The `PocketTTSHandler` accepts an optional `voice_embedding` argument in its request payload. When present, Pocket TTS renders output speech using the cloned voice characteristics rather than default voices.

**3. Integrate into the Pipeline**

The embedding attaches to the `VoicePrompt` object, which travels with the LM response through the pipeline and arrives at the TTS handler. This design allows you to swap Pocket TTS for alternative backends without modifying surrounding code.

## Implementation Guide

### Install Dependencies

Begin by installing the speech-to-speech library and Pocket TTS client:

```bash
pip install speech-to-speech pocket-tts

```

### Generate Speaker Embeddings

Create a speaker embedding from reference audio using your preferred voice encoder:

```python
import torch
from your_cloner import VoiceEncoder  # Replace with your actual encoder library

encoder = VoiceEncoder(pretrained="my-voice-encoder")
ref_audio = torch.load("reference.wav")          # waveform tensor, shape (1, samples)

speaker_emb = encoder.encode(ref_audio)          # → tensor of shape (1, 256)

```

### Configure Pocket TTS Arguments

Initialize the pipeline with `PocketTTSArguments` to specify model paths and embedding dimensions:

```python
from speech_to_speech.arguments_classes.pocket_tts_arguments import PocketTTSArguments
from speech_to_speech.s2s_pipeline import SpeechToSpeechPipeline

tts_args = PocketTTSArguments(
    model_path="pocket-tts-lite",    # local model name or remote identifier

    api_url="http://localhost:8000", # if running Pocket TTS as a server

    voice_embedding_dim=256,          # must match encoder output size

)

pipeline = SpeechToSpeechPipeline(
    stt_args=...,          # your STT configuration

    lm_args=...,           # language-model arguments

    tts_args=tts_args,
)

```

### Inject Custom Voice Embeddings

Pass the speaker embedding through the `VoicePrompt` class to enable voice cloning:

```python
from speech_to_speech.pipeline.messages import VoicePrompt

# Build prompt with speaker embedding

prompt = VoicePrompt(
    text="Hello, this is a cloned voice speaking.", 
    voice_embedding=speaker_emb.numpy().tolist()   # Pocket TTS expects JSON-serializable format

)

# Execute full pipeline: audio → text → LM → TTS

audio_out = pipeline.run(prompt)   # returns NumPy array or AudioSegment

```

### Handle Output Audio

Save or play the synthesized audio. Pocket TTS defaults to 24 kHz sampling rate:

```python
import soundfile as sf
sf.write("output.wav", audio_out, samplerate=24_000)

```

### Dynamic Voice Switching

Update voice embeddings mid-conversation without restarting the pipeline:

```python

# Generate new embedding from updated reference audio

new_emb = encoder.encode(torch.load("new_ref.wav"))

# Update pipeline state dynamically

pipeline.update_voice_embedding(new_emb.numpy().tolist())

# Subsequent TTS outputs use the new cloned voice

```

## Key Source Files and Their Roles

Understanding the source structure helps when extending or debugging voice cloning functionality.

- **[`src/speech_to_speech/TTS/pocket_tts_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/TTS/pocket_tts_handler.py)** – Contains the `PocketTTSHandler` class implementing the `handle` method. This file manages communication with Pocket TTS backends and processes `voice_embedding` parameters.

- **[`src/speech_to_speech/arguments_classes/pocket_tts_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/pocket_tts_arguments.py)** – Defines the `PocketTTSArguments` dataclass exposing configuration knobs including `model_path`, `api_url`, and `voice_embedding_dim`.

- **[`src/speech_to_speech/s2s_pipeline.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/s2s_pipeline.py)** – Houses the `SpeechToSpeechPipeline` class that orchestrates STT → LM → TTS flow, handling message routing through the pipeline's `Control` mechanisms.

- **[`src/speech_to_speech/pipeline/messages.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/pipeline/messages.py)** – Defines `VoicePrompt` and other message types that transport voice embeddings and text through the pipeline stages.

- **[`src/speech_to_speech/baseHandler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/baseHandler.py)** – Provides the abstract base class ensuring all handlers implement the consistent `handle` contract required by the pipeline.

## Summary

- The **huggingface/speech-to-speech** repository implements voice cloning through a decoupled handler architecture centered on `PocketTTSHandler` and `VoicePrompt` classes.

- **Speaker embeddings** generated from reference audio attach to `VoicePrompt` objects and flow through [`src/speech_to_speech/s2s_pipeline.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/s2s_pipeline.py) to reach the TTS backend.

- **Configuration** occurs via `PocketTTSArguments` in [`src/speech_to_speech/arguments_classes/pocket_tts_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/pocket_tts_arguments.py), supporting both local models and REST API endpoints.

- **Dynamic switching** allows real-time voice changes without pipeline restarts using the `update_voice_embedding` method.

- The default **24 kHz** output sample rate from Pocket TTS requires appropriate audio handling when saving or streaming results.

## Frequently Asked Questions

### What audio format should speaker embeddings use for Pocket TTS?

Pocket TTS expects voice embeddings as JSON-serializable lists or arrays, typically converted from PyTorch tensors or NumPy arrays using `.tolist()` or `.numpy().tolist()` methods. The embedding dimension must match the `voice_embedding_dim` parameter specified in `PocketTTSArguments`, commonly 256 dimensions.

### Can I switch between multiple cloned voices during a single conversation?

Yes. The pipeline supports dynamic voice switching through the `update_voice_embedding` method. Generate new speaker embeddings from different reference audio files and call this method between utterances. The `CancelScope` utilities in [`src/speech_to_speech/s2s_pipeline.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/s2s_pipeline.py) allow you to interrupt current synthesis and replace it with the new voice immediately.

### Does Pocket TTS require a local server or can it run remotely?

Pocket TTS supports both deployment modes. Configure local execution by setting `model_path` to a local checkpoint in `PocketTTSArguments`. For remote execution, specify the `api_url` pointing to your Pocket TTS server endpoint. The `PocketTTSHandler` in [`src/speech_to_speech/TTS/pocket_tts_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/TTS/pocket_tts_handler.py) handles HTTP communication automatically when `api_url` is provided.

### How does the pipeline handle voice cloning failures or missing embeddings?

When `voice_embedding` is None or invalid, `PocketTTSHandler` falls back to default voice synthesis specified in the `model_path` or `voice_id` configuration. The `VoicePrompt` class validates message structure in [`src/speech_to_speech/pipeline/messages.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/pipeline/messages.py), ensuring the pipeline continues operating even when custom voice data is unavailable.