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Running
on
Zero
| from queue import Queue | |
| from transformers.generation.streamers import BaseStreamer | |
| from typing import Optional | |
| from parler_tts import ParlerTTSForConditionalGeneration | |
| import numpy as np | |
| import math | |
| import torch | |
| 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 |