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funasr1.0.5 (#1328)

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zhifu gao 2024-01-31 16:21:10 +08:00 committed by GitHub
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6 changed files with 74 additions and 52 deletions
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13
examples/industrial_data_pretraining/conformer/demo.py Normal file
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@ -0,0 +1,13 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
from funasr import AutoModel
model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch", model_revision="v2.0.4",
)
res = model.generate(input="https://modelscope.oss-cn-beijing.aliyuncs.com/test/audios/asr_example.wav")
print(res)

11
examples/industrial_data_pretraining/conformer/infer.sh Normal file
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@ -0,0 +1,11 @@
model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch"
model_revision="v2.0.4"
python funasr/bin/inference.py \
+model=${model} \
+model_revision=${model_revision} \
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
+output_dir="./outputs/debug" \
+device="cpu" \

12
funasr/models/conformer/template.yaml
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@ -6,8 +6,7 @@
# tables.print()
# network architecture
#model: funasr.models.paraformer.model:Paraformer
model: Transformer
model: Conformer
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1 # label smoothing option
@ -16,14 +15,14 @@ model_conf:
# encoder
encoder: ConformerEncoder
encoder_conf:
output_size: 256 # dimension of attention
output_size: 256
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder architecture type
input_layer: conv2d
normalize_before: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
@ -52,6 +51,7 @@ frontend_conf:
n_mels: 80
frame_length: 25
frame_shift: 10
dither: 0.0
lfr_m: 1
lfr_n: 1

87
funasr/models/transformer/model.py
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@ -24,18 +24,16 @@ class Transformer(nn.Module):
def __init__(
self,
frontend: Optional[str] = None,
frontend_conf: Optional[Dict] = None,
specaug: Optional[str] = None,
specaug_conf: Optional[Dict] = None,
specaug: str = None,
specaug_conf: dict = None,
normalize: str = None,
normalize_conf: Optional[Dict] = None,
normalize_conf: dict = None,
encoder: str = None,
encoder_conf: Optional[Dict] = None,
encoder_conf: dict = None,
decoder: str = None,
decoder_conf: Optional[Dict] = None,
decoder_conf: dict = None,
ctc: str = None,
ctc_conf: Optional[Dict] = None,
ctc_conf: dict = None,
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
input_size: int = 80,
@ -59,20 +57,17 @@ class Transformer(nn.Module):
super().__init__()
if frontend is not None:
frontend_class = tables.frontend_classes.get_class(frontend)
frontend = frontend_class(**frontend_conf)
if specaug is not None:
specaug_class = tables.specaug_classes.get_class(specaug)
specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
normalize_class = tables.normalize_classes.get_class(normalize)
normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
encoder_class = tables.encoder_classes.get_class(encoder)
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
if decoder is not None:
decoder_class = tables.decoder_classes.get_class(decoder)
decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@ -93,7 +88,6 @@ class Transformer(nn.Module):
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
@ -338,6 +332,7 @@ class Transformer(nn.Module):
)
token_list = kwargs.get("token_list")
scorers.update(
decoder=self.decoder,
length_bonus=LengthBonus(len(token_list)),
)
@ -348,14 +343,14 @@ class Transformer(nn.Module):
scorers["ngram"] = ngram
weights = dict(
decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.0),
ctc=kwargs.get("decoding_ctc_weight", 0.0),
decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
ctc=kwargs.get("decoding_ctc_weight", 0.5),
lm=kwargs.get("lm_weight", 0.0),
ngram=kwargs.get("ngram_weight", 0.0),
length_bonus=kwargs.get("penalty", 0.0),
)
beam_search = BeamSearch(
beam_size=kwargs.get("beam_size", 2),
beam_size=kwargs.get("beam_size", 10),
weights=weights,
scorers=scorers,
sos=self.sos,
@ -364,17 +359,15 @@ class Transformer(nn.Module):
token_list=token_list,
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
)
# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
# for scorer in scorers.values():
# if isinstance(scorer, torch.nn.Module):
# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
self.beam_search = beam_search
def generate(self,
data_in: list,
data_lengths: list=None,
def inference(self,
data_in,
data_lengths=None,
key: list=None,
tokenizer=None,
frontend=None,
**kwargs,
):
@ -382,27 +375,34 @@ class Transformer(nn.Module):
raise NotImplementedError("batch decoding is not implemented")
# init beamsearch
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
if self.beam_search is None and (is_use_lm or is_use_ctc):
if self.beam_search is None:
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
audio_sample_list = load_audio_text_image_video(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
speech, speech_lengths = data_in, data_lengths
if len(speech.shape) < 3:
speech = speech[None, :, :]
if speech_lengths is None:
speech_lengths = speech.shape[1]
else:
# extract fbank feats
time1 = time.perf_counter()
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
data_type=kwargs.get("data_type", "sound"),
tokenizer=tokenizer)
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
frontend=frontend)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
@ -439,14 +439,13 @@ class Transformer(nn.Module):
token = tokenizer.ids2tokens(token_int)
text = tokenizer.tokens2text(token)
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
# text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
result_i = {"key": key[i], "token": token, "text": text}
results.append(result_i)
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["text"][key[i]] = text
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
return results, meta_data

2
funasr/models/transformer/search.py
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@ -9,7 +9,7 @@ from typing import Union
import torch
from funasr.metrics import end_detect
from funasr.metrics.common import end_detect
from funasr.models.transformer.scorers.scorer_interface import PartialScorerInterface
from funasr.models.transformer.scorers.scorer_interface import ScorerInterface

1
funasr/models/transformer/template.yaml
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@ -6,7 +6,6 @@
# tables.print()
# network architecture
#model: funasr.models.paraformer.model:Paraformer
model: Transformer
model_conf:
ctc_weight: 0.3