indic-parler-tts

📖 Quick Index

• 👨‍💻 Installation
• 🛠️ Key capabilities
• 🎲 Using a random voice
• 🌍 Switching languages
• 🎯 Using a specific speaker
• Some Description Examples
• 📐Evaluation
• Motivation
• Optimizing inference

👨‍💻 Installation

Using Parler-TTS is as simple as "bonjour". Simply install the library once:

pip install git+https://github.com/huggingface/parler-tts.git

🛠️ Key capabilities

The model accepts two primary inputs:

1. Transcript - The text to be converted to speech.
2. Caption - A detailed description of how the speech should sound, e.g., "Leela speaks in a high-pitched, fast-paced, and cheerful tone, full of energy and happiness. The recording is very high quality with no background noise."

Key Features

1. Language Support

   • Officially supported languages: Assamese, Bengali, Bodo, Dogri, Kannada, Malayalam, Marathi, Sanskrit, Nepali, English, Telugu, Hindi, Gujarati, Konkani, Maithili, Manipuri, Odia, Santali, Sindhi, Tamil, and Urdu.
   • Unofficial support: Chhattisgarhi, Kashmiri, Punjabi.

2. Speaker Diversity

   • 69 unique voices across the supported languages.
   • Supported languages have a set of recommended voices optimized for naturalness and intelligibility.

3. Emotion Rendering

   • 10 languages officially support emotion-specific prompts: Assamese, Bengali, Bodo, Dogri, Kannada, Malayalam, Marathi, Sanskrit, Nepali, and Tamil.
   • Emotion support for other languages exists but has not been extensively tested.
   • Available emotions include: Command, Anger, Narration, Conversation, Disgust, Fear, Happy, Neutral, Proper Noun, News, Sad, and Surprise.

4. Accent Flexibility

   • The model officially supports Indian English accents through its English voices, providing clear and natural speech.
   • For other accents, the model allows customization by specifying accent details, such as "A male British speaker" or "A female American speaker," using style transfer for more dynamic and personalized outputs.

5. Customizable Output Indic Parler-TTS offers precise control over various speech characteristics using the caption input:

   • Background Noise: Adjust the noise level in the audio, from clear to slightly noisy environments.
   • Reverberation: Control the perceived distance of the voice, from close-sounding to distant-sounding speech.
   • Expressivity: Specify how dynamic or monotone the speech should be, ranging from expressive to slightly expressive or monotone.
   • Pitch: Modify the pitch of the speech, including high, low, or balanced tones.
   • Speaking Rate: Change the speaking rate, from slow to fast.
   • Voice Quality: Control the overall clarity and naturalness of the speech, adjusting from basic to refined voice quality.

🎲 Random voice

🚨 Unlike previous versions of Parler-TTS, here we use two tokenizers - one for the prompt and one for the description. 🚨

Indic Parler-TTS has been trained to generate speech with features that can be controlled with a simple text prompt, for example:

import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = ParlerTTSForConditionalGeneration.from_pretrained("ai4bharat/indic-parler-tts").to(device)
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indic-parler-tts")
description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)

prompt = "Hey, how are you doing today?"
description = "A female speaker with a British accent delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."

description_input_ids = description_tokenizer(description, return_tensors="pt").to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").to(device)

generation = model.generate(input_ids=description_input_ids.input_ids, attention_mask=description_input_ids.attention_mask, prompt_input_ids=prompt_input_ids.input_ids, prompt_attention_mask=prompt_input_ids.attention_mask)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("indic_tts_out.wav", audio_arr, model.config.sampling_rate)

Indic Parler-TTS provides highly effective control over key aspects of speech synthesis using descriptive captions. Below is a summary of what each control parameter can achieve: