321 lines
11 KiB
Python
321 lines
11 KiB
Python
import json
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import re
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from collections import OrderedDict
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from pathlib import Path
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from typing import Union
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import torch
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import numpy as np
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import torch.nn.functional as F
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from whisper.tokenizer import get_tokenizer
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from whisper_live.tensorrt_utils import (mel_filters, load_audio_wav_format, pad_or_trim, load_audio)
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import tensorrt_llm
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import tensorrt_llm.logger as logger
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from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
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trt_dtype_to_torch)
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from tensorrt_llm.runtime import ModelConfig, SamplingConfig
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from tensorrt_llm.runtime.session import Session, TensorInfo
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SAMPLE_RATE = 16000
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N_FFT = 400
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HOP_LENGTH = 160
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CHUNK_LENGTH = 30
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N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000 samples in a 30-second chunk
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class WhisperEncoding:
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def __init__(self, engine_dir):
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self.session = self.get_session(engine_dir)
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def get_session(self, engine_dir):
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config_path = engine_dir / 'encoder_config.json'
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with open(config_path, 'r') as f:
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config = json.load(f)
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dtype = config['builder_config']['precision']
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n_mels = config['builder_config']['n_mels']
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num_languages = config['builder_config']['num_languages']
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self.dtype = dtype
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self.n_mels = n_mels
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self.num_languages = num_languages
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serialize_path = engine_dir / f'whisper_encoder_{self.dtype}_tp1_rank0.engine'
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with open(serialize_path, 'rb') as f:
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session = Session.from_serialized_engine(f.read())
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return session
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def get_audio_features(self, mel):
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inputs = OrderedDict()
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output_list = []
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inputs.update({'x': mel})
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output_list.append(
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TensorInfo('x', str_dtype_to_trt(self.dtype), mel.shape))
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output_info = (self.session).infer_shapes(output_list)
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logger.debug(f'output info {output_info}')
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outputs = {
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t.name: torch.empty(tuple(t.shape),
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dtype=trt_dtype_to_torch(t.dtype),
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device='cuda')
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for t in output_info
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}
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stream = torch.cuda.current_stream()
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ok = self.session.run(inputs=inputs,
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outputs=outputs,
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stream=stream.cuda_stream)
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assert ok, 'Engine execution failed'
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stream.synchronize()
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audio_features = outputs['output']
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return audio_features
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class WhisperDecoding:
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def __init__(self, engine_dir, runtime_mapping, debug_mode=False):
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self.decoder_config = self.get_config(engine_dir)
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self.decoder_generation_session = self.get_session(
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engine_dir, runtime_mapping, debug_mode)
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def get_config(self, engine_dir):
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config_path = engine_dir / 'decoder_config.json'
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with open(config_path, 'r') as f:
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config = json.load(f)
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decoder_config = OrderedDict()
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decoder_config.update(config['plugin_config'])
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decoder_config.update(config['builder_config'])
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return decoder_config
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def get_session(self, engine_dir, runtime_mapping, debug_mode=False):
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dtype = self.decoder_config['precision']
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serialize_path = engine_dir / f'whisper_decoder_{dtype}_tp1_rank0.engine'
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with open(serialize_path, "rb") as f:
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decoder_engine_buffer = f.read()
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decoder_model_config = ModelConfig(
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num_heads=self.decoder_config['num_heads'],
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num_kv_heads=self.decoder_config['num_heads'],
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hidden_size=self.decoder_config['hidden_size'],
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vocab_size=self.decoder_config['vocab_size'],
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num_layers=self.decoder_config['num_layers'],
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gpt_attention_plugin=self.decoder_config['gpt_attention_plugin'],
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remove_input_padding=self.decoder_config['remove_input_padding'],
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cross_attention=self.decoder_config['cross_attention'],
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has_position_embedding=self.
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decoder_config['has_position_embedding'],
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has_token_type_embedding=self.
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decoder_config['has_token_type_embedding'],
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)
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decoder_generation_session = tensorrt_llm.runtime.GenerationSession(
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decoder_model_config,
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decoder_engine_buffer,
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runtime_mapping,
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debug_mode=debug_mode)
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return decoder_generation_session
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def generate(self,
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decoder_input_ids,
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encoder_outputs,
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eot_id,
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max_new_tokens=40,
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num_beams=1):
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encoder_input_lengths = torch.tensor(
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[encoder_outputs.shape[1] for x in range(encoder_outputs.shape[0])],
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dtype=torch.int32,
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device='cuda')
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decoder_input_lengths = torch.tensor([
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decoder_input_ids.shape[-1]
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for _ in range(decoder_input_ids.shape[0])
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],
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dtype=torch.int32,
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device='cuda')
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decoder_max_input_length = torch.max(decoder_input_lengths).item()
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# generation config
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sampling_config = SamplingConfig(end_id=eot_id,
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pad_id=eot_id,
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num_beams=num_beams)
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self.decoder_generation_session.setup(
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decoder_input_lengths.size(0),
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decoder_max_input_length,
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max_new_tokens,
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beam_width=num_beams,
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encoder_max_input_length=encoder_outputs.shape[1])
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torch.cuda.synchronize()
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decoder_input_ids = decoder_input_ids.type(torch.int32).cuda()
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output_ids = self.decoder_generation_session.decode(
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decoder_input_ids,
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decoder_input_lengths,
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sampling_config,
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encoder_output=encoder_outputs,
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encoder_input_lengths=encoder_input_lengths,
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)
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torch.cuda.synchronize()
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# get the list of int from output_ids tensor
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output_ids = output_ids.cpu().numpy().tolist()
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return output_ids
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class WhisperTRTLLM(object):
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def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
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language="en", task="transcribe"):
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world_size = 1
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runtime_rank = tensorrt_llm.mpi_rank()
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runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
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torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
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engine_dir = Path(engine_dir)
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self.encoder = WhisperEncoding(engine_dir)
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self.decoder = WhisperDecoding(engine_dir,
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runtime_mapping,
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debug_mode=False)
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self.n_mels = self.encoder.n_mels
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# self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
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# tokenizer_dir=assets_dir)
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self.device = device
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self.tokenizer = get_tokenizer(
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is_multilingual,
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num_languages=self.encoder.num_languages,
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language=language,
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task=task,
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)
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self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)
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def log_mel_spectrogram(
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self,
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audio: Union[str, np.ndarray, torch.Tensor],
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padding: int = 0,
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return_duration=True
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):
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"""
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Compute the log-Mel spectrogram of
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Parameters
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----------
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audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
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The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
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n_mels: int
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The number of Mel-frequency filters, only 80 and 128 are supported
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padding: int
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Number of zero samples to pad to the right
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device: Optional[Union[str, torch.device]]
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If given, the audio tensor is moved to this device before STFT
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Returns
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-------
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torch.Tensor, shape = (80 or 128, n_frames)
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A Tensor that contains the Mel spectrogram
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"""
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if not torch.is_tensor(audio):
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if isinstance(audio, str):
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if audio.endswith('.wav'):
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audio, _ = load_audio_wav_format(audio)
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else:
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audio = load_audio(audio)
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assert isinstance(audio, np.ndarray), f"Unsupported audio type: {type(audio)}"
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duration = audio.shape[-1] / SAMPLE_RATE
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audio = pad_or_trim(audio, N_SAMPLES)
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audio = audio.astype(np.float32)
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audio = torch.from_numpy(audio)
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if self.device is not None:
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audio = audio.to(self.device)
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if padding > 0:
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audio = F.pad(audio, (0, padding))
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window = torch.hann_window(N_FFT).to(audio.device)
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stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
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magnitudes = stft[..., :-1].abs()**2
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mel_spec = self.filters @ magnitudes
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log_spec = torch.clamp(mel_spec, min=1e-10).log10()
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log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
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log_spec = (log_spec + 4.0) / 4.0
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if return_duration:
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return log_spec, duration
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else:
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return log_spec
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def process_batch(
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self,
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mel,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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num_beams=1):
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prompt_id = self.tokenizer.encode(
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text_prefix, allowed_special=set(self.tokenizer.special_tokens.keys()))
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prompt_id = torch.tensor(prompt_id)
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batch_size = mel.shape[0]
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decoder_input_ids = prompt_id.repeat(batch_size, 1)
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encoder_output = self.encoder.get_audio_features(mel)
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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self.tokenizer.eot,
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max_new_tokens=96,
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num_beams=num_beams)
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texts = []
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for i in range(len(output_ids)):
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text = self.tokenizer.decode(output_ids[i][0]).strip()
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texts.append(text)
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return texts
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def transcribe(
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self,
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mel,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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dtype='float16',
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batch_size=1,
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num_beams=1,
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):
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mel = mel.type(str_dtype_to_torch(dtype))
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mel = mel.unsqueeze(0)
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predictions = self.process_batch(mel, text_prefix, num_beams)
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prediction = predictions[0]
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# remove all special tokens in the prediction
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prediction = re.sub(r'<\|.*?\|>', '', prediction)
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return prediction.strip()
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def decode_wav_file(
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model,
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mel,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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dtype='float16',
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batch_size=1,
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num_beams=1,
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normalizer=None,
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mel_filters_dir=None):
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mel = mel.type(str_dtype_to_torch(dtype))
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mel = mel.unsqueeze(0)
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# repeat the mel spectrogram to match the batch size
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mel = mel.repeat(batch_size, 1, 1)
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predictions = model.process_batch(mel, text_prefix, num_beams)
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prediction = predictions[0]
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# remove all special tokens in the prediction
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prediction = re.sub(r'<\|.*?\|>', '', prediction)
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if normalizer:
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prediction = normalizer(prediction)
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return prediction.strip()
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