whisper_live/trt_transcriber.py

import argparse
import json
import re
import time
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union

import torch
import numpy as np
from whisper.tokenizer import get_tokenizer
from whisper_live.whisper_utils import (mel_filters, store_transcripts,
                           write_error_stats, load_audio_wav_format,
                           pad_or_trim)

import tensorrt_llm
import tensorrt_llm.logger as logger
from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
                                 trt_dtype_to_torch)
from tensorrt_llm.runtime import ModelConfig, SamplingConfig
from tensorrt_llm.runtime.session import Session, TensorInfo


SAMPLE_RATE = 16000
N_FFT = 400
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk


class WhisperEncoding:

    def __init__(self, engine_dir):
        self.session = self.get_session(engine_dir)

    def get_session(self, engine_dir):
        config_path = engine_dir / 'encoder_config.json'
        with open(config_path, 'r') as f:
            config = json.load(f)

        use_gpt_attention_plugin = config['plugin_config'][
            'gpt_attention_plugin']
        dtype = config['builder_config']['precision']
        n_mels = config['builder_config']['n_mels']
        num_languages = config['builder_config']['num_languages']

        self.dtype = dtype
        self.n_mels = n_mels
        self.num_languages = num_languages

        serialize_path = engine_dir / f'whisper_encoder_{self.dtype}_tp1_rank0.engine'

        with open(serialize_path, 'rb') as f:
            session = Session.from_serialized_engine(f.read())

        return session

    def get_audio_features(self, mel):
        inputs = OrderedDict()
        output_list = []

        inputs.update({'x': mel})
        output_list.append(
            TensorInfo('x', str_dtype_to_trt(self.dtype), mel.shape))

        output_info = (self.session).infer_shapes(output_list)

        logger.debug(f'output info {output_info}')
        outputs = {
            t.name: torch.empty(tuple(t.shape),
                                dtype=trt_dtype_to_torch(t.dtype),
                                device='cuda')
            for t in output_info
        }
        stream = torch.cuda.current_stream()
        ok = self.session.run(inputs=inputs,
                              outputs=outputs,
                              stream=stream.cuda_stream)
        assert ok, 'Engine execution failed'
        stream.synchronize()
        audio_features = outputs['output']
        return audio_features


class WhisperDecoding:

    def __init__(self, engine_dir, runtime_mapping, debug_mode=False):

        self.decoder_config = self.get_config(engine_dir)
        self.decoder_generation_session = self.get_session(
            engine_dir, runtime_mapping, debug_mode)

    def get_config(self, engine_dir):
        config_path = engine_dir / 'decoder_config.json'
        with open(config_path, 'r') as f:
            config = json.load(f)
        decoder_config = OrderedDict()
        decoder_config.update(config['plugin_config'])
        decoder_config.update(config['builder_config'])
        return decoder_config

    def get_session(self, engine_dir, runtime_mapping, debug_mode=False):
        dtype = self.decoder_config['precision']
        serialize_path = engine_dir / f'whisper_decoder_{dtype}_tp1_rank0.engine'
        with open(serialize_path, "rb") as f:
            decoder_engine_buffer = f.read()

        decoder_model_config = ModelConfig(
            num_heads=self.decoder_config['num_heads'],
            num_kv_heads=self.decoder_config['num_heads'],
            hidden_size=self.decoder_config['hidden_size'],
            vocab_size=self.decoder_config['vocab_size'],
            num_layers=self.decoder_config['num_layers'],
            gpt_attention_plugin=self.decoder_config['gpt_attention_plugin'],
            remove_input_padding=self.decoder_config['remove_input_padding'],
            cross_attention=self.decoder_config['cross_attention'],
            has_position_embedding=self.
            decoder_config['has_position_embedding'],
            has_token_type_embedding=self.
            decoder_config['has_token_type_embedding'],
        )
        decoder_generation_session = tensorrt_llm.runtime.GenerationSession(
            decoder_model_config,
            decoder_engine_buffer,
            runtime_mapping,
            debug_mode=debug_mode)

        return decoder_generation_session

    def generate(self,
                 decoder_input_ids,
                 encoder_outputs,
                 eot_id,
                 max_new_tokens=40,
                 num_beams=1):
        encoder_input_lengths = torch.tensor(
            [encoder_outputs.shape[1] for x in range(encoder_outputs.shape[0])],
            dtype=torch.int32,
            device='cuda')

        decoder_input_lengths = torch.tensor([
            decoder_input_ids.shape[-1]
            for _ in range(decoder_input_ids.shape[0])
        ],
                                             dtype=torch.int32,
                                             device='cuda')
        decoder_max_input_length = torch.max(decoder_input_lengths).item()

        # generation config
        sampling_config = SamplingConfig(end_id=eot_id,
                                         pad_id=eot_id,
                                         num_beams=num_beams)
        self.decoder_generation_session.setup(
            decoder_input_lengths.size(0),
            decoder_max_input_length,
            max_new_tokens,
            beam_width=num_beams,
            encoder_max_input_length=encoder_outputs.shape[1])

        torch.cuda.synchronize()

        decoder_input_ids = decoder_input_ids.type(torch.int32).cuda()
        output_ids = self.decoder_generation_session.decode(
            decoder_input_ids,
            decoder_input_lengths,
            sampling_config,
            encoder_output=encoder_outputs,
            encoder_input_lengths=encoder_input_lengths,
        )
        torch.cuda.synchronize()

        # get the list of int from output_ids tensor
        output_ids = output_ids.cpu().numpy().tolist()
        return output_ids


class WhisperTRTLLM(object):

    def __init__(
        self,
        engine_dir,
        debug_mode=False,
        assets_dir=None,
        device=None
        ):
        world_size = 1
        runtime_rank = tensorrt_llm.mpi_rank()
        runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
        torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)
        engine_dir = Path(engine_dir)

        self.encoder = WhisperEncoding(engine_dir)
        self.decoder = WhisperDecoding(engine_dir,
                                       runtime_mapping,
                                       debug_mode=False)
        self.n_mels = self.encoder.n_mels
        # self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
        #                                tokenizer_dir=assets_dir)
        self.device = device
        self.tokenizer = get_tokenizer(
            False,
            num_languages=self.encoder.num_languages,
            language="en",
            task="transcribe",
        )
        self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)

    def log_mel_spectrogram(
        self,
        audio: Union[str, np.ndarray, torch.Tensor],
        padding: int = 0,
        return_duration = True
    ):
        """
        Compute the log-Mel spectrogram of

        Parameters
        ----------
        audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
            The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz

        n_mels: int
            The number of Mel-frequency filters, only 80 and 128 are supported

        padding: int
            Number of zero samples to pad to the right

        device: Optional[Union[str, torch.device]]
            If given, the audio tensor is moved to this device before STFT

        Returns
        -------
        torch.Tensor, shape = (80 or 128, n_frames)
            A Tensor that contains the Mel spectrogram
        """
        if not torch.is_tensor(audio):
            if isinstance(audio, str):
                if audio.endswith('.wav'):
                    audio, _ = load_audio_wav_format(audio)
                else:
                    audio = load_audio(audio)
            assert isinstance(audio,
                            np.ndarray), f"Unsupported audio type: {type(audio)}"
            duration = audio.shape[-1] / SAMPLE_RATE
            audio = pad_or_trim(audio, N_SAMPLES)
            audio = audio.astype(np.float32)
            audio = torch.from_numpy(audio)

        if self.device is not None:
            audio = audio.to(self.device)
        if padding > 0:
            audio = F.pad(audio, (0, padding))
        window = torch.hann_window(N_FFT).to(audio.device)
        stft = torch.stft(audio,
                        N_FFT,
                        HOP_LENGTH,
                        window=window,
                        return_complex=True)
        magnitudes = stft[..., :-1].abs()**2

        
        mel_spec = self.filters @ magnitudes

        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
        log_spec = (log_spec + 4.0) / 4.0
        if return_duration:
            return log_spec, duration
        else:
            return log_spec


    def process_batch(
            self,
            mel,
            text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
            num_beams=1):
        prompt_id = self.tokenizer.encode(
            text_prefix, allowed_special=set(self.tokenizer.special_tokens.keys()))

        prompt_id = torch.tensor(prompt_id)
        batch_size = mel.shape[0]
        decoder_input_ids = prompt_id.repeat(batch_size, 1)

        encoder_output = self.encoder.get_audio_features(mel)
        output_ids = self.decoder.generate(decoder_input_ids,
                                           encoder_output,
                                           self.tokenizer.eot,
                                           max_new_tokens=96,
                                           num_beams=num_beams)
        texts = []
        for i in range(len(output_ids)):
            text = self.tokenizer.decode(output_ids[i][0]).strip()
            texts.append(text)
        return texts
    
    def transcribe(
            self,
            mel,
            text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
            dtype='float16',
            batch_size=1,
            num_beams=1,
            ):
        mel = mel.type(str_dtype_to_torch(dtype))
        mel = mel.unsqueeze(0)
        predictions = self.process_batch(mel, text_prefix, num_beams)
        prediction = predictions[0]

        # remove all special tokens in the prediction
        prediction = re.sub(r'<\|.*?\|>', '', prediction)
        return prediction.strip()


def decode_wav_file(
        model,
        mel,
        text_prefix="<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
        dtype='float16',
        batch_size=1,
        num_beams=1,
        normalizer=None,
        mel_filters_dir=None):

    mel = mel.type(str_dtype_to_torch(dtype))
    mel = mel.unsqueeze(0)
    # repeat the mel spectrogram to match the batch size
    mel = mel.repeat(batch_size, 1, 1)
    predictions = model.process_batch(mel, text_prefix, num_beams)
    prediction = predictions[0]

    # remove all special tokens in the prediction
    prediction = re.sub(r'<\|.*?\|>', '', prediction)
    if normalizer:
        prediction = normalizer(prediction)
    
    return prediction.strip()


if __name__=="__main__":
    tensorrt_llm.logger.set_level("error")
    model = WhisperTRTLLM("../whisper_small_en", False, "../assets", device="cuda")
    mel, total_duration = model.log_mel_spectrogram(
        "/root/Code/outputs/output3.wav",
    )
    results = model.transcribe(mel)
    print(results, total_duration)