import io 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 pydub import AudioSegment 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" jenny_repo_id = "ylacombe/parler-tts-mini-jenny-30H" model = ParlerTTSForConditionalGeneration.from_pretrained( repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ).to(device) jenny_model = ParlerTTSForConditionalGeneration.from_pretrained( jenny_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.", 3.0, ], [ "'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.", 3.0, ], [ "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.", 3.0, ], [ "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.", 3.0, ], ] jenny_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.", "Jenny speaks at an average pace with a slightly animated delivery in a very confined sounding environment with clear audio quality.", 3.0, ], [ "'This is the best time of my life, Bartley,' she said happily.", "Jenny speaks in quite a monotone voice at a slightly faster-than-average pace in a confined space with very clear audio.", 3.0, ], [ "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.", "Jenny delivers her words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.", 3.0, ], [ "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.", "Jenny delivers her words at a fast pace and an animated tone, in a very spacious environment, accompanied by noticeable background noise.", 3.0, ], ] 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 Parler) _, 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) decode_sequentially = ( self.generation_config.bos_token_id in input_ids or self.generation_config.pad_token_id in input_ids or self.generation_config.eos_token_id in input_ids ) if not decode_sequentially: output_values = self.audio_encoder.decode( input_ids, audio_scales=[None], ) else: sample = input_ids[:, 0] sample_mask = (sample >= self.audio_encoder.config.codebook_size).sum(dim=(0, 1)) == 0 sample = sample[:, :, sample_mask] output_values = self.audio_encoder.decode(sample[None, ...], [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("ParlerTTSStreamer 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 def numpy_to_mp3(audio_array, sampling_rate): # Normalize audio_array if it's floating-point if np.issubdtype(audio_array.dtype, np.floating): max_val = np.max(np.abs(audio_array)) audio_array = (audio_array / max_val) * 32767 # Normalize to 16-bit range audio_array = audio_array.astype(np.int16) # Create an audio segment from the numpy array audio_segment = AudioSegment( audio_array.tobytes(), frame_rate=sampling_rate, sample_width=audio_array.dtype.itemsize, channels=1 ) # Export the audio segment to MP3 bytes - use a high bitrate to maximise quality mp3_io = io.BytesIO() audio_segment.export(mp3_io, format="mp3", bitrate="320k") # Get the MP3 bytes mp3_bytes = mp3_io.getvalue() mp3_io.close() return mp3_bytes sampling_rate = model.audio_encoder.config.sampling_rate frame_rate = model.audio_encoder.config.frame_rate @spaces.GPU def generate_base(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") yield numpy_to_mp3(new_audio, sampling_rate=sampling_rate) @spaces.GPU def generate_jenny(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=jenny_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") 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( """

Parler-TTS 🗣️

""" ) gr.HTML( f"""

Parler-TTS is a training and inference library for high-fidelity text-to-speech (TTS) models. Two models are demonstrated here, Parler-TTS Mini v0.1, is the first iteration model trained using 10k hours of narrated audiobooks, and Parler-TTS Jenny, a model fine-tuned on the Jenny dataset. Both models 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:

""" ) with gr.Tab("Base"): 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") play_seconds = gr.Slider(3.0, 7.0, value=3.0, step=2, label="Streaming interval in seconds", info="Lower = shorter chunks, lower latency, more codec steps") run_button = gr.Button("Generate Audio", variant="primary") with gr.Column(): audio_out = gr.Audio(label="Parler-TTS generation", format="mp3", elem_id="audio_out", streaming=True, autoplay=True) inputs = [input_text, description, play_seconds] outputs = [audio_out] gr.Examples(examples=examples, fn=generate_base, inputs=inputs, outputs=outputs, cache_examples=False) run_button.click(fn=generate_base, inputs=inputs, outputs=outputs, queue=True) with gr.Tab("Jenny"): with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="Input Text", lines=2, value=jenny_examples[0][0], elem_id="input_text") description = gr.Textbox(label="Description", lines=2, value=jenny_examples[0][1], elem_id="input_description") play_seconds = gr.Slider(3.0, 7.0, value=jenny_examples[0][2], step=2, label="Streaming interval in seconds", info="Lower = shorter chunks, lower latency, more codec steps") run_button = gr.Button("Generate Audio", variant="primary") with gr.Column(): audio_out = gr.Audio(label="Parler-TTS generation", format="mp3", elem_id="audio_out", streaming=True, autoplay=True) inputs = [input_text, description, play_seconds] outputs = [audio_out] gr.Examples(examples=jenny_examples, fn=generate_jenny, inputs=inputs, outputs=outputs, cache_examples=False) run_button.click(fn=generate_jenny, 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)