Note
Click here to download the full example code
ASR Inference with CUDA CTC Decoder¶
Author: Yuekai Zhang
This tutorial shows how to perform speech recognition inference using a CUDA-based CTC beam search decoder. We demonstrate this on a pretrained Zipformer model from Next-gen Kaldi project.
Overview¶
Beam search decoding works by iteratively expanding text hypotheses (beams) with next possible characters, and maintaining only the hypotheses with the highest scores at each time step.
- The underlying implementation uses cuda to acclerate the whole decoding process
A mathematical formula for the decoder can be
found in the paper, and a more detailed algorithm can be found in this blog.
Running ASR inference using a CUDA CTC Beam Search decoder requires the following components
Acoustic Model: model predicting modeling units (BPE in this tutorial) from acoustic features
BPE Model: the byte-pair encoding (BPE) tokenizer file
Acoustic Model and Set Up¶
First we import the necessary utilities and fetch the data that we are working with
import torch
import torchaudio
print(torch.__version__)
print(torchaudio.__version__)
2.3.0
2.3.0
import time
from pathlib import Path
import IPython
import sentencepiece as spm
from torchaudio.models.decoder import cuda_ctc_decoder
from torchaudio.utils import download_asset
We use the pretrained Zipformer model that is trained on the LibriSpeech dataset. The model is jointly trained with CTC and Transducer loss functions. In this tutorial, we only use CTC head of the model.
def download_asset_external(url, key):
path = Path(torch.hub.get_dir()) / "torchaudio" / Path(key)
if not path.exists():
path.parent.mkdir(parents=True, exist_ok=True)
torch.hub.download_url_to_file(url, path)
return str(path)
url_prefix = "https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01"
model_link = f"{url_prefix}/resolve/main/exp/cpu_jit.pt"
model_path = download_asset_external(model_link, "cuda_ctc_decoder/cpu_jit.pt")
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We will load a sample from the LibriSpeech test-other dataset.
speech_file = download_asset("tutorial-assets/ctc-decoding/1688-142285-0007.wav")
waveform, sample_rate = torchaudio.load(speech_file)
assert sample_rate == 16000
IPython.display.Audio(speech_file)
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The transcript corresponding to this audio file is
Files and Data for Decoder¶
Next, we load in our token from BPE model, which is the tokenizer for decoding.
Tokens¶
The tokens are the possible symbols that the acoustic model can predict, including the blank symbol in CTC. In this tutorial, it includes 500 BPE tokens. It can either be passed in as a file, where each line consists of the tokens corresponding to the same index, or as a list of tokens, each mapping to a unique index.
# tokens
<blk>
<sos/eos>
<unk>
S
_THE
_A
T
_AND
...
bpe_link = f"{url_prefix}/resolve/main/data/lang_bpe_500/bpe.model"
bpe_path = download_asset_external(bpe_link, "cuda_ctc_decoder/bpe.model")
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(bpe_path)
tokens = [bpe_model.id_to_piece(id) for id in range(bpe_model.get_piece_size())]
print(tokens)
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['<blk>', '<sos/eos>', '<unk>', 'S', '▁THE', '▁A', 'T', '▁AND', 'ED', '▁OF', '▁TO', 'E', 'D', 'N', 'ING', '▁IN', 'Y', 'M', 'C', '▁I', 'A', 'P', '▁HE', 'R', 'O', 'L', 'RE', 'I', 'U', 'ER', '▁IT', 'LY', '▁THAT', '▁WAS', '▁', '▁S', 'AR', '▁BE', 'F', '▁C', 'IN', 'B', '▁FOR', 'OR', 'LE', "'", '▁HIS', '▁YOU', 'AL', '▁RE', 'V', '▁B', 'G', 'RI', '▁E', '▁WITH', '▁T', '▁AS', 'LL', '▁P', '▁HER', 'ST', '▁HAD', '▁SO', '▁F', 'W', 'CE', '▁IS', 'ND', '▁NOT', 'TH', '▁BUT', 'EN', '▁SHE', '▁ON', 'VE', 'ON', 'SE', '▁DE', 'UR', '▁G', 'CH', 'K', 'TER', '▁AT', 'IT', '▁ME', 'RO', 'NE', 'RA', 'ES', 'IL', 'NG', 'IC', '▁NO', '▁HIM', 'ENT', 'IR', '▁WE', 'H', '▁DO', '▁ALL', '▁HAVE', 'LO', '▁BY', '▁MY', '▁MO', '▁THIS', 'LA', '▁ST', '▁WHICH', '▁CON', '▁THEY', 'CK', 'TE', '▁SAID', '▁FROM', '▁GO', '▁WHO', '▁TH', '▁OR', '▁D', '▁W', 'VER', 'LI', '▁SE', '▁ONE', '▁CA', '▁AN', '▁LA', '▁WERE', 'EL', '▁HA', '▁MAN', '▁FA', '▁EX', 'AD', '▁SU', 'RY', '▁MI', 'AT', '▁BO', '▁WHEN', 'AN', 'THER', 'PP', 'ATION', '▁FI', '▁WOULD', '▁PRO', 'OW', 'ET', '▁O', '▁THERE', '▁HO', 'ION', '▁WHAT', '▁FE', '▁PA', 'US', 'MENT', '▁MA', 'UT', '▁OUT', '▁THEIR', '▁IF', '▁LI', '▁K', '▁WILL', '▁ARE', 'ID', '▁RO', 'DE', 'TION', '▁WA', 'PE', '▁UP', '▁SP', '▁PO', 'IGHT', '▁UN', 'RU', '▁LO', 'AS', 'OL', '▁LE', '▁BEEN', '▁SH', '▁RA', '▁SEE', 'KE', 'UL', 'TED', '▁SA', 'UN', 'UND', 'ANT', '▁NE', 'IS', '▁THEM', 'CI', 'GE', '▁COULD', '▁DIS', 'OM', 'ISH', 'HE', 'EST', '▁SOME', 'ENCE', 'ITY', 'IVE', '▁US', '▁MORE', '▁EN', 'ARD', 'ATE', '▁YOUR', '▁INTO', '▁KNOW', '▁CO', 'ANCE', '▁TIME', '▁WI', '▁YE', 'AGE', '▁NOW', 'TI', 'FF', 'ABLE', '▁VERY', '▁LIKE', 'AM', 'HI', 'Z', '▁OTHER', '▁THAN', '▁LITTLE', '▁DID', '▁LOOK', 'TY', 'ERS', '▁CAN', '▁CHA', '▁AR', 'X', 'FUL', 'UGH', '▁BA', '▁DAY', '▁ABOUT', 'TEN', 'IM', '▁ANY', '▁PRE', '▁OVER', 'IES', 'NESS', 'ME', 'BLE', '▁M', 'ROW', '▁HAS', '▁GREAT', '▁VI', 'TA', '▁AFTER', 'PER', '▁AGAIN', 'HO', 'SH', '▁UPON', '▁DI', '▁HAND', '▁COM', 'IST', 'TURE', '▁STA', '▁THEN', '▁SHOULD', '▁GA', 'OUS', 'OUR', '▁WELL', '▁ONLY', 'MAN', '▁GOOD', '▁TWO', '▁MAR', '▁SAY', '▁HU', 'TING', '▁OUR', 'RESS', '▁DOWN', 'IOUS', '▁BEFORE', '▁DA', '▁NA', 'QUI', '▁MADE', '▁EVERY', '▁OLD', '▁EVEN', 'IG', '▁COME', '▁GRA', '▁RI', '▁LONG', 'OT', 'SIDE', 'WARD', '▁FO', '▁WHERE', 'MO', 'LESS', '▁SC', '▁MUST', '▁NEVER', '▁HOW', '▁CAME', '▁SUCH', '▁RU', '▁TAKE', '▁WO', '▁CAR', 'UM', 'AK', '▁THINK', '▁MUCH', '▁MISTER', '▁MAY', '▁JO', '▁WAY', '▁COMP', '▁THOUGHT', '▁STO', '▁MEN', '▁BACK', '▁DON', 'J', '▁LET', '▁TRA', '▁FIRST', '▁JUST', '▁VA', '▁OWN', '▁PLA', '▁MAKE', 'ATED', '▁HIMSELF', '▁WENT', '▁PI', 'GG', 'RING', '▁DU', '▁MIGHT', '▁PART', '▁GIVE', '▁IMP', '▁BU', '▁PER', '▁PLACE', '▁HOUSE', '▁THROUGH', 'IAN', '▁SW', '▁UNDER', 'QUE', '▁AWAY', '▁LOVE', 'QUA', '▁LIFE', '▁GET', '▁WITHOUT', '▁PASS', '▁TURN', 'IGN', '▁HEAD', '▁MOST', '▁THOSE', '▁SHALL', '▁EYES', '▁COL', '▁STILL', '▁NIGHT', '▁NOTHING', 'ITION', 'HA', '▁TELL', '▁WORK', '▁LAST', '▁NEW', '▁FACE', '▁HI', '▁WORD', '▁FOUND', '▁COUNT', '▁OB', '▁WHILE', '▁SHA', '▁MEAN', '▁SAW', '▁PEOPLE', '▁FRIEND', '▁THREE', '▁ROOM', '▁SAME', '▁THOUGH', '▁RIGHT', '▁CHILD', '▁FATHER', '▁ANOTHER', '▁HEART', '▁WANT', '▁TOOK', 'OOK', '▁LIGHT', '▁MISSUS', '▁OPEN', '▁JU', '▁ASKED', 'PORT', '▁LEFT', '▁JA', '▁WORLD', '▁HOME', '▁WHY', '▁ALWAYS', '▁ANSWER', '▁SEEMED', '▁SOMETHING', '▁GIRL', '▁BECAUSE', '▁NAME', '▁TOLD', '▁NI', '▁HIGH', 'IZE', '▁WOMAN', '▁FOLLOW', '▁RETURN', '▁KNEW', '▁EACH', '▁KIND', '▁JE', '▁ACT', '▁LU', '▁CERTAIN', '▁YEARS', '▁QUITE', '▁APPEAR', '▁BETTER', '▁HALF', '▁PRESENT', '▁PRINCE', 'SHIP', '▁ALSO', '▁BEGAN', '▁HAVING', '▁ENOUGH', '▁PERSON', '▁LADY', '▁WHITE', '▁COURSE', '▁VOICE', '▁SPEAK', '▁POWER', '▁MORNING', '▁BETWEEN', '▁AMONG', '▁KEEP', '▁WALK', '▁MATTER', '▁TEA', '▁BELIEVE', '▁SMALL', '▁TALK', '▁FELT', '▁HORSE', '▁MYSELF', '▁SIX', '▁HOWEVER', '▁FULL', '▁HERSELF', '▁POINT', '▁STOOD', '▁HUNDRED', '▁ALMOST', '▁SINCE', '▁LARGE', '▁LEAVE', '▁PERHAPS', '▁DARK', '▁SUDDEN', '▁REPLIED', '▁ANYTHING', '▁WONDER', '▁UNTIL', 'Q']
Construct CUDA Decoder¶
In this tutorial, we will construct a CUDA beam search decoder.
The decoder can be constructed using the factory function
cuda_ctc_decoder().
Run Inference¶
Now that we have the data, acoustic model, and decoder, we can perform
inference. The output of the beam search decoder is of type
CUCTCHypothesis, consisting of the
predicted token IDs, words (symbols corresponding to the token IDs), and hypothesis scores.
Recall the transcript corresponding to the
waveform is
actual_transcript = "i really was very much afraid of showing him how much shocked i was at some parts of what he said"
actual_transcript = actual_transcript.split()
device = torch.device("cuda", 0)
acoustic_model = torch.jit.load(model_path)
acoustic_model.to(device)
acoustic_model.eval()
waveform = waveform.to(device)
feat = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=80, snip_edges=False)
feat = feat.unsqueeze(0)
feat_lens = torch.tensor(feat.size(1), device=device).unsqueeze(0)
encoder_out, encoder_out_lens = acoustic_model.encoder(feat, feat_lens)
nnet_output = acoustic_model.ctc_output(encoder_out)
log_prob = torch.nn.functional.log_softmax(nnet_output, -1)
print(f"The shape of log_prob: {log_prob.shape}, the shape of encoder_out_lens: {encoder_out_lens.shape}")
The shape of log_prob: torch.Size([1, 175, 500]), the shape of encoder_out_lens: torch.Size([1])
The cuda ctc decoder gives the following result.
results = cuda_decoder(log_prob, encoder_out_lens.to(torch.int32))
beam_search_transcript = bpe_model.decode(results[0][0].tokens).lower()
beam_search_wer = torchaudio.functional.edit_distance(actual_transcript, beam_search_transcript.split()) / len(
actual_transcript
)
print(f"Transcript: {beam_search_transcript}")
print(f"WER: {beam_search_wer}")
Transcript: i really was very much afraid of showing him how much shocked i was at some parts of what he said
WER: 0.0
Beam Search Decoder Parameters¶
In this section, we go a little bit more in depth about some different
parameters and tradeoffs. For the full list of customizable parameters,
please refer to the
documentation.
Helper Function¶
def print_decoded(cuda_decoder, bpe_model, log_prob, encoder_out_lens, param, param_value):
start_time = time.monotonic()
results = cuda_decoder(log_prob, encoder_out_lens.to(torch.int32))
decode_time = time.monotonic() - start_time
transcript = bpe_model.decode(results[0][0].tokens).lower()
score = results[0][0].score
print(f"{param} {param_value:<3}: {transcript} (score: {score:.2f}; {decode_time:.4f} secs)")
nbest¶
This parameter indicates the number of best hypotheses to return. For
instance, by setting nbest=10 when constructing the beam search
decoder earlier, we can now access the hypotheses with the top 10 scores.
i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.2029460221529007)
i really was very much afraid of showing him how much shocked i was at some part of what he said (score: -1.7402369976043701)
i really was very much afraid of sheowing him how much shocked i was at some parts of what he said (score: -6.679358005523682)
i reallyly very much afraid of showing him how much shocked i was at some parts of what he said (score: -7.596949577331543)
i really was very much afraid of sheowing him how much shocked i was at some part of what he said (score: -8.223165512084961)
i really was very much afraid of shwing him how much shocked i was at some parts of what he said (score: -8.439875602722168)
i really was very much afraid of showing him how much shocked i was in some parts of what he said (score: -8.782379150390625)
i really was very much afraid of showing him how much shocked i was at some parts of what said (score: -8.884151458740234)
i really was very much afraid of showing him how much shocked i was at some partes of what he said (score: -8.999359130859375)
i really was very much afraid of showing him how much shocked i was at some parts of what he say (score: -9.138347625732422)
beam size¶
The beam_size parameter determines the maximum number of best
hypotheses to hold after each decoding step. Using larger beam sizes
allows for exploring a larger range of possible hypotheses which can
produce hypotheses with higher scores, but it does not provide additional gains beyond a certain point.
We recommend to set beam_size=10 for cuda beam search decoder.
In the example below, we see improvement in decoding quality as we increase beam size from 1 to 3, but notice how using a beam size of 3 provides the same output as beam size 10.
beam_sizes = [1, 2, 3, 10]
for beam_size in beam_sizes:
beam_search_decoder = cuda_ctc_decoder(
tokens,
nbest=1,
beam_size=beam_size,
blank_skip_threshold=0.95,
)
print_decoded(beam_search_decoder, bpe_model, log_prob, encoder_out_lens, "beam size", beam_size)
beam size 1 : i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -1.35; 0.0009 secs)
beam size 2 : i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.21; 0.0009 secs)
beam size 3 : i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.20; 0.0009 secs)
beam size 10 : i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.20; 0.0010 secs)
blank skip threshold¶
The blank_skip_threshold parameter is used to prune the frames which have large blank probability.
Pruning these frames with a good blank_skip_threshold could speed up decoding
process a lot while no accuracy drop.
Since the rule of CTC, we would keep at least one blank frame between two non-blank frames
to avoid mistakenly merge two consecutive identical symbols.
We recommend to set blank_skip_threshold=0.95 for cuda beam search decoder.
blank_skip_probs = [0.25, 0.95, 1.0]
for blank_skip_prob in blank_skip_probs:
beam_search_decoder = cuda_ctc_decoder(
tokens,
nbest=10,
beam_size=10,
blank_skip_threshold=blank_skip_prob,
)
print_decoded(beam_search_decoder, bpe_model, log_prob, encoder_out_lens, "blank_skip_threshold", blank_skip_prob)
del cuda_decoder
blank_skip_threshold 0.25: i really was very much afraid of showing him how much shocked i was at some part of what he said (score: -0.01; 0.0009 secs)
blank_skip_threshold 0.95: i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.20; 0.0010 secs)
blank_skip_threshold 1.0: i really was very much afraid of showing him how much shocked i was at some parts of what he said (score: -0.21; 0.0044 secs)
Benchmark with flashlight CPU decoder¶
We benchmark the throughput and accuracy between CUDA decoder and CPU decoder using librispeech test_other set. To reproduce below benchmark results, you may refer here.
Decoder |
Setting |
WER (%) |
N-Best Oracle WER (%) |
Decoder Cost Time (seconds) |
|---|---|---|---|---|
CUDA decoder |
blank_skip_threshold 0.95 |
5.81 |
4.11 |
2.57 |
CUDA decoder |
blank_skip_threshold 1.0 (no frame-skip) |
5.81 |
4.09 |
6.24 |
CPU decoder |
beam_size_token 10 |
5.86 |
4.30 |
28.61 |
CPU decoder |
beam_size_token 500 |
5.86 |
4.30 |
791.80 |
From the above table, CUDA decoder could give a slight improvement in WER and a significant increase in throughput.
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