import math
from queue import Queue
from threading import Thread
from typing import Optional
import numpy as np
import spaces
import gradio as gr
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
from transformers.generation.streamers import BaseStreamer
device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
torch_dtype = torch.float16 if device != "cpu" else torch.float32
repo_id = "parler-tts/parler_tts_mini_v0.1"
model = ParlerTTSForConditionalGeneration.from_pretrained(
repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True
).to(device)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
SAMPLE_RATE = feature_extractor.sampling_rate
SEED = 42
default_text = "Please surprise me and speak in whatever voice you enjoy."
examples = [
[
"Remember - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of five times.",
"A male speaker with a low-pitched voice delivering his words at a fast pace in a small, confined space with a very clear audio and an animated tone."
],
[
"'This is the best time of my life, Bartley,' she said happily.",
"A female speaker with a slightly low-pitched, quite monotone voice delivers her words at a slightly faster-than-average pace in a confined space with very clear audio.",
],
[
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
"A male speaker with a slightly high-pitched voice delivering his words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.",
],
[
"Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
"A male speaker with a low-pitched voice delivers his words at a fast pace and an animated tone, in a very spacious environment, accompanied by noticeable background noise.",
],
]
class ParlerTTSStreamer(BaseStreamer):
def __init__(
self,
model: ParlerTTSForConditionalGeneration,
device: Optional[str] = None,
play_steps: Optional[int] = 10,
stride: Optional[int] = None,
timeout: Optional[float] = None,
):
"""
Streamer that stores playback-ready audio in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from accessing the generated audio in a non-blocking way (e.g. in an interactive
Gradio demo).
Parameters:
model (`ParlerTTSForConditionalGeneration`):
The Parler-TTS model used to generate the audio waveform.
device (`str`, *optional*):
The torch device on which to run the computation. If `None`, will default to the device of the model.
play_steps (`int`, *optional*, defaults to 10):
The number of generation steps with which to return the generated audio array. Using fewer steps will
mean the first chunk is ready faster, but will require more codec decoding steps overall. This value
should be tuned to your device and latency requirements.
stride (`int`, *optional*):
The window (stride) between adjacent audio samples. Using a stride between adjacent audio samples reduces
the hard boundary between them, giving smoother playback. If `None`, will default to a value equivalent to
play_steps // 6 in the audio space.
timeout (`int`, *optional*):
The timeout for the audio queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
"""
self.decoder = model.decoder
self.audio_encoder = model.audio_encoder
self.generation_config = model.generation_config
self.device = device if device is not None else model.device
# variables used in the streaming process
self.play_steps = play_steps
if stride is not None:
self.stride = stride
else:
hop_length = math.floor(self.audio_encoder.config.sampling_rate / self.audio_encoder.config.frame_rate)
self.stride = hop_length * (play_steps - self.decoder.num_codebooks) // 6
self.token_cache = None
self.to_yield = 0
# varibles used in the thread process
self.audio_queue = Queue()
self.stop_signal = None
self.timeout = timeout
def apply_delay_pattern_mask(self, input_ids):
# build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
_, delay_pattern_mask = self.decoder.build_delay_pattern_mask(
input_ids[:, :1],
bos_token_id=self.generation_config.bos_token_id,
pad_token_id=self.generation_config.decoder_start_token_id,
max_length=input_ids.shape[-1],
)
# apply the pattern mask to the input ids
input_ids = self.decoder.apply_delay_pattern_mask(input_ids, delay_pattern_mask)
# revert the pattern delay mask by filtering the pad token id
mask = (delay_pattern_mask != self.generation_config.bos_token_id) & (delay_pattern_mask != self.generation_config.pad_token_id)
input_ids = input_ids[mask].reshape(1, self.decoder.num_codebooks, -1)
# append the frame dimension back to the audio codes
input_ids = input_ids[None, ...]
# send the input_ids to the correct device
input_ids = input_ids.to(self.audio_encoder.device)
output_values = self.audio_encoder.decode(
input_ids,
audio_scales=[None],
)
audio_values = output_values.audio_values[0, 0]
return audio_values.cpu().float().numpy()
def put(self, value):
batch_size = value.shape[0] // self.decoder.num_codebooks
if batch_size > 1:
raise ValueError("MusicgenStreamer only supports batch size 1")
if self.token_cache is None:
self.token_cache = value
else:
self.token_cache = torch.concatenate([self.token_cache, value[:, None]], dim=-1)
if self.token_cache.shape[-1] % self.play_steps == 0:
audio_values = self.apply_delay_pattern_mask(self.token_cache)
self.on_finalized_audio(audio_values[self.to_yield : -self.stride])
self.to_yield += len(audio_values) - self.to_yield - self.stride
def end(self):
"""Flushes any remaining cache and appends the stop symbol."""
if self.token_cache is not None:
audio_values = self.apply_delay_pattern_mask(self.token_cache)
else:
audio_values = np.zeros(self.to_yield)
self.on_finalized_audio(audio_values[self.to_yield :], stream_end=True)
def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False):
"""Put the new audio in the queue. If the stream is ending, also put a stop signal in the queue."""
self.audio_queue.put(audio, timeout=self.timeout)
if stream_end:
self.audio_queue.put(self.stop_signal, timeout=self.timeout)
def __iter__(self):
return self
def __next__(self):
value = self.audio_queue.get(timeout=self.timeout)
if not isinstance(value, np.ndarray) and value == self.stop_signal:
raise StopIteration()
else:
return value
sampling_rate = model.audio_encoder.config.sampling_rate
frame_rate = model.audio_encoder.config.frame_rate
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
@spaces.GPU
def generate_tts(text, description, play_steps_in_s=2.0):
play_steps = int(frame_rate * play_steps_in_s)
streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)
inputs = tokenizer(description, return_tensors="pt").to(device)
prompt = tokenizer(text, return_tensors="pt").to(device)
generation_kwargs = dict(
input_ids=inputs.input_ids,
prompt_input_ids=prompt.input_ids,
streamer=streamer,
do_sample=True,
temperature=1.0,
min_new_tokens=10,
)
set_seed(SEED)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
for new_audio in streamer:
print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds")
new_audio = (new_audio * max_range).astype(np.int16)
yield sampling_rate, new_audio
css = """
#share-btn-container {
display: flex;
padding-left: 0.5rem !important;
padding-right: 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
width: 13rem;
margin-top: 10px;
margin-left: auto;
flex: unset !important;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor: pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding-top: 0.25rem !important;
padding-bottom: 0.25rem !important;
right:0;
}
#share-btn * {
all: unset !important;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
"""
with gr.Blocks(css=css) as block:
gr.HTML(
"""
"""
)
gr.HTML(
f"""
Parler-TTS is a training and inference library for
high-fidelity text-to-speech (TTS) models. The model demonstrated here, Parler-TTS Mini v0.1,
is the first iteration model trained using 10k hours of narrated audiobooks. It generates high-quality speech
with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
Tips for ensuring good generation:
- Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
- Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
- The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
"""
)
with gr.Row():
with gr.Column():
input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
description = gr.Textbox(label="Description", lines=2, value="", elem_id="input_description")
run_button = gr.Button("Generate Audio", variant="primary")
with gr.Column():
audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out", streaming=True, autoplay=True)
inputs = [input_text, description]
outputs = [audio_out]
gr.Examples(examples=examples, fn=generate_tts, inputs=inputs, outputs=outputs, cache_examples=False)
run_button.click(fn=generate_tts, inputs=inputs, outputs=outputs, queue=True)
gr.HTML(
"""
To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech.
The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention
and torch compile, that will improve the latency by 2-4x. If you want to find out more about how this model was trained and even fine-tune it yourself, check-out the
Parler-TTS repository on GitHub.
The Parler-TTS codebase and its associated checkpoints are licensed under Apache 2.0.
"""
)
block.queue()
block.launch(share=True)