From: aidotengineer
Voice cloning is a technique used in text-to-speech (TTS) models to generate speech that sounds like a specific individual’s voice [17:58:00]. It differs from traditional fine-tuning in its approach [18:00:00].
How Voice Cloning Works
In voice cloning, a sample of the target voice is provided to the model [18:04:00]. The model then uses this audio sample as a reference to generate new speech, aiming to match the characteristics of the input voice [18:10:00]. The generated audio will tend to sound more like the voice in the sample provided [18:17:00].
Comparison with Zero-Shot Inference
Without voice cloning (referred to as “zero-shot inference”), a text-to-speech model, especially a token-based text to speech model like Sesame CSM1B, might generate speech with a random speaker’s voice if the “temperature” parameter is non-zero [17:48:00]. This means the output could be a female or male voice, deep or high-pitched, and generally unpredictable [17:50:00]. Voice cloning helps to avoid this randomness by guiding the model with a specific voice sample [17:58:00].
Comparison with Fine-Tuning
While voice cloning can make the output voice much closer to the desired speaker than zero-shot inference, it typically won’t achieve the same level of resemblance as dedicated fine-tuning [18:42:00]. However, combining fine-tuning with voice cloning can yield excellent performance, even with a relatively small amount of data [33:14:00].
Practical Implementation
In a practical scenario, such as using the Sesame CSM1B model, a dataset containing audio snippets and their transcriptions can be used for voice cloning [18:22:00]. After loading this dataset, a sample from it can be passed to the model, which then generates text [18:30:00].
For example, an initial zero-shot generation might produce a woman’s voice for a given text [20:46:00]. When the same text is generated with voice cloning using a male speaker’s sample, the output voice will be male and closer to the sample’s characteristics [21:28:00].
While voice cloning alone improves voice resemblance, fine-tuning the model on a specific voice dataset further enhances this similarity, producing an even more accurate representation of the target voice, especially when combined with cloning [21:50:00], [32:16:00]. Even with a relatively small amount of data (e.g., a 30-minute video), combining fine-tuning and voice cloning can achieve good results [33:19:00].
To further improve performance, particularly without relying on voice cloning, it is recommended to aim for larger datasets, such as 500 rows of 30-second audio snippets [33:04:00].