makaveli10
427 lines
16 KiB
Python
427 lines
16 KiB
Python
import json
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import re
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import math
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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.transcriber.tensorrt_utils import (
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mel_filters,
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load_audio_wav_format,
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pad_or_trim,
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load_audio
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)
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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.bindings import GptJsonConfig, KVCacheType
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from tensorrt_llm.runtime import PYTHON_BINDINGS, 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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def read_config(component, engine_dir):
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config_path = engine_dir / component / '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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model_config = OrderedDict()
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model_config.update(config['pretrained_config'])
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model_config.update(config['build_config'])
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return model_config
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def remove_tensor_padding(input_tensor,
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input_tensor_lengths=None,
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pad_value=None):
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if pad_value:
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assert input_tensor_lengths is None, "input_tensor_lengths should be None when pad_value is provided"
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# Text tensor case: batch, seq_len
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assert torch.all(
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input_tensor[:, 0] != pad_value
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), "First token in each sequence should not be pad_value"
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assert input_tensor_lengths is None
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# Create a mask for all non-pad tokens
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mask = input_tensor != pad_value
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# Apply the mask to input_tensor to remove pad tokens
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output_tensor = input_tensor[mask].view(1, -1)
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else:
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# Audio tensor case: batch, seq_len, feature_len
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# position_ids case: batch, seq_len
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assert input_tensor_lengths is not None, "input_tensor_lengths must be provided for 3D input_tensor"
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# Initialize a list to collect valid sequences
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valid_sequences = []
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for i in range(input_tensor.shape[0]):
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valid_length = input_tensor_lengths[i]
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valid_sequences.append(input_tensor[i, :valid_length])
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# Concatenate all valid sequences along the batch dimension
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output_tensor = torch.cat(valid_sequences, dim=0)
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return output_tensor
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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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config = read_config('encoder', engine_dir)
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self.n_mels = config['n_mels']
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self.dtype = config['dtype']
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self.num_languages = config['num_languages']
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self.encoder_config = config
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def get_session(self, engine_dir):
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serialize_path = engine_dir / 'encoder' / '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,
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mel,
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mel_input_lengths,
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encoder_downsampling_factor=2):
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if isinstance(mel, list):
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longest_mel = max([f.shape[-1] for f in mel])
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mel = [
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torch.nn.functional.pad(f, (0, longest_mel - f.shape[-1]),
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mode='constant') for f in mel
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]
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mel = torch.cat(mel, dim=0).type(
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str_dtype_to_torch("float16")).contiguous()
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bsz, seq_len = mel.shape[0], mel.shape[2]
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position_ids = torch.arange(
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math.ceil(seq_len / encoder_downsampling_factor),
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dtype=torch.int32,
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device=mel.device).expand(bsz, -1).contiguous()
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if self.encoder_config['plugin_config']['remove_input_padding']:
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# mel B,D,T -> B,T,D -> BxT, D
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mel = mel.transpose(1, 2)
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mel = remove_tensor_padding(mel, mel_input_lengths)
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position_ids = remove_tensor_padding(
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position_ids, mel_input_lengths // encoder_downsampling_factor)
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inputs = OrderedDict()
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inputs['input_features'] = mel
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inputs['input_lengths'] = mel_input_lengths
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inputs['position_ids'] = position_ids
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output_list = [
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TensorInfo('input_features', str_dtype_to_trt(self.dtype),
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mel.shape),
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TensorInfo('input_lengths', str_dtype_to_trt('int32'),
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mel_input_lengths.shape),
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TensorInfo('position_ids', str_dtype_to_trt('int32'),
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inputs['position_ids'].shape)
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]
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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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encoder_output = outputs['encoder_output']
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encoder_output_lengths = mel_input_lengths // encoder_downsampling_factor
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return encoder_output, encoder_output_lengths
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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 = read_config('decoder', 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_session(self, engine_dir, runtime_mapping, debug_mode=False):
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serialize_path = engine_dir / 'decoder' / '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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max_batch_size=self.decoder_config['max_batch_size'],
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max_beam_width=self.decoder_config['max_beam_width'],
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num_heads=self.decoder_config['num_attention_heads'],
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num_kv_heads=self.decoder_config['num_attention_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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cross_attention=True,
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num_layers=self.decoder_config['num_hidden_layers'],
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gpt_attention_plugin=self.decoder_config['plugin_config']
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['gpt_attention_plugin'],
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remove_input_padding=self.decoder_config['plugin_config']
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['remove_input_padding'],
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kv_cache_type=KVCacheType.PAGED
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if self.decoder_config['plugin_config']['paged_kv_cache'] == True
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else KVCacheType.CONTINUOUS,
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has_position_embedding=self.
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decoder_config['has_position_embedding'],
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dtype=self.decoder_config['dtype'],
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has_token_type_embedding=False,
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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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encoder_max_input_length,
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encoder_input_lengths,
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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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batch_size = decoder_input_ids.shape[0]
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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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cross_attention_mask = torch.ones([
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batch_size, decoder_max_input_length + max_new_tokens,
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encoder_max_input_length
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]).int().cuda()
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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_max_input_length)
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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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if self.decoder_config['plugin_config']['remove_input_padding']:
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# 50256 is the index of <pad> for all whisper models' decoder
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WHISPER_PAD_TOKEN_ID = 50256
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decoder_input_ids = remove_tensor_padding(
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decoder_input_ids, pad_value=WHISPER_PAD_TOKEN_ID)
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if encoder_outputs.dim() == 3:
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encoder_output_lens = torch.full((encoder_outputs.shape[0], ),
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encoder_outputs.shape[1],
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dtype=torch.int32,
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device='cuda')
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encoder_outputs = remove_tensor_padding(encoder_outputs,
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encoder_output_lens)
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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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cross_attention_mask=cross_attention_mask,
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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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encoder_config = read_config('encoder', engine_dir)
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decoder_config = read_config('decoder', engine_dir)
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self.n_mels = encoder_config['n_mels']
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self.num_languages = encoder_config['num_languages']
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is_multilingual = (decoder_config['vocab_size'] >= 51865)
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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.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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mel_input_lengths,
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text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
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num_beams=1,
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max_new_tokens=96):
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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, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
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encoder_max_input_length = torch.max(encoder_output_lengths).item()
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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encoder_max_input_length,
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encoder_output_lengths,
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self.tokenizer.eot,
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max_new_tokens=max_new_tokens,
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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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padding_strategy="max",
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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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# repeat the mel spectrogram to match the batch size
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mel = mel.repeat(batch_size, 1, 1)
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if padding_strategy == "longest":
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pass
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else:
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mel = torch.nn.functional.pad(mel, (0, 3000 - mel.shape[2]))
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features_input_lengths = torch.full((mel.shape[0], ),
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mel.shape[2],
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dtype=torch.int32,
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device=mel.device)
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predictions = self.process_batch(mel, features_input_lengths, 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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