From 5a8f37908469d9550f905ba0876c7c4e6f9b8026 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 21 十二月 2023 21:08:46 +0800
Subject: [PATCH] vad + asr
---
funasr/bin/inference.py | 198 ++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 177 insertions(+), 21 deletions(-)
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index d7b33e3..fda7abe 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -16,7 +16,8 @@
import random
import string
from funasr.register import tables
-
+from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio
+from funasr.utils.vad_utils import slice_padding_audio_samples
def build_iter_for_infer(data_in, input_len=None, data_type="sound"):
"""
@@ -73,15 +74,44 @@
logging.basicConfig(level=log_level)
- import pdb;
- pdb.set_trace()
+ if kwargs.get("debug", False):
+ import pdb; pdb.set_trace()
model = AutoModel(**kwargs)
- res = model.generate(input=kwargs["input"])
+ res = model(input=kwargs["input"])
print(res)
class AutoModel:
+
def __init__(self, **kwargs):
tables.print()
+
+ model, kwargs = self.build_model(**kwargs)
+
+ # if vad_model is not None, build vad model else None
+ vad_model = kwargs.get("vad_model", None)
+ vad_kwargs = kwargs.get("vad_model_revision", None)
+ if vad_model is not None:
+ print("build vad model")
+ vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+ vad_model, vad_kwargs = self.build_model(**vad_kwargs)
+
+ # if punc_model is not None, build punc model else None
+ punc_model = kwargs.get("punc_model", None)
+ punc_kwargs = kwargs.get("punc_model_revision", None)
+ if punc_model is not None:
+ punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+ punc_model, punc_kwargs = self.build_model(**punc_kwargs)
+
+ self.kwargs = kwargs
+ self.model = model
+ self.vad_model = vad_model
+ self.vad_kwargs = vad_kwargs
+ self.punc_model = punc_model
+ self.punc_kwargs = punc_kwargs
+
+
+
+ def build_model(self, **kwargs):
assert "model" in kwargs
if "model_conf" not in kwargs:
logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
@@ -94,7 +124,7 @@
device = "cpu"
kwargs["batch_size"] = 1
kwargs["device"] = device
-
+
# build tokenizer
tokenizer = kwargs.get("tokenizer", None)
if tokenizer is not None:
@@ -113,7 +143,8 @@
# build model
model_class = tables.model_classes.get(kwargs["model"].lower())
- model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
+ model = model_class(**kwargs, **kwargs["model_conf"],
+ vocab_size=len(tokenizer.token_list) if tokenizer is not None else -1)
model.eval()
model.to(device)
@@ -127,23 +158,34 @@
ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
oss_bucket=kwargs.get("oss_bucket", None),
)
- self.kwargs = kwargs
- self.model = model
- self.tokenizer = tokenizer
+
+ return model, kwargs
- def generate(self, input, input_len=None, **cfg):
- self.kwargs.update(cfg)
- data_type = self.kwargs.get("data_type", "sound")
- batch_size = self.kwargs.get("batch_size", 1)
- if self.kwargs.get("device", "cpu") == "cpu":
- batch_size = 1
+ def __call__(self, input, input_len=None, **cfg):
+ if self.vad_model is None:
+ return self.generate(input, input_len=input_len, **cfg)
+
+ else:
+ return self.generate_with_vad(input, input_len=input_len, **cfg)
+
+ def generate(self, input, input_len=None, model=None, kwargs=None, **cfg):
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+ model = self.model if model is None else model
+
+ data_type = kwargs.get("data_type", "sound")
+ batch_size = kwargs.get("batch_size", 1)
+ # if kwargs.get("device", "cpu") == "cpu":
+ # batch_size = 1
key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
speed_stats = {}
asr_result_list = []
num_samples = len(data_list)
- pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
+ pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
+ time_speech_total = 0.0
+ time_escape_total = 0.0
for beg_idx in range(0, num_samples, batch_size):
end_idx = min(num_samples, beg_idx + batch_size)
data_batch = data_list[beg_idx:end_idx]
@@ -154,25 +196,139 @@
batch["data_lengths"] = input_len
time1 = time.perf_counter()
- results, meta_data = self.model.generate(**batch, **self.kwargs)
+ results, meta_data = model.generate(**batch, **kwargs)
time2 = time.perf_counter()
- asr_result_list.append(results)
+ asr_result_list.extend(results)
pbar.update(1)
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
batch_data_time = meta_data.get("batch_data_time", -1)
+ time_escape = time2 - time1
speed_stats["load_data"] = meta_data.get("load_data", 0.0)
speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
- speed_stats["forward"] = f"{time2 - time1:0.3f}"
- speed_stats["rtf"] = f"{(time2 - time1) / batch_data_time:0.3f}"
+ speed_stats["forward"] = f"{time_escape:0.3f}"
+ speed_stats["batch_size"] = f"{len(results)}"
+ speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
description = (
f"{speed_stats}, "
)
pbar.set_description(description)
-
+ time_speech_total += batch_data_time
+ time_escape_total += time_escape
+
+ pbar.update(1)
+ pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
torch.cuda.empty_cache()
return asr_result_list
+
+ def generate_with_vad(self, input, input_len=None, **cfg):
+
+ # step.1: compute the vad model
+ model = self.vad_model
+ kwargs = self.vad_kwargs
+ beg_vad = time.time()
+ res = self.generate(input, input_len=input_len, model=model, kwargs=kwargs, **cfg)
+ end_vad = time.time()
+ print(f"time cost vad: {end_vad - beg_vad:0.3f}")
+
+ # step.2 compute asr model
+ model = self.model
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ batch_size = int(kwargs.get("batch_size_s", 300))*1000
+ batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
+ kwargs["batch_size"] = batch_size
+ data_type = kwargs.get("data_type", "sound")
+ key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
+ results_ret_list = []
+ time_speech_total_all_samples = 0.0
+
+ beg_total = time.time()
+ pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+ for i in range(len(res)):
+ key = res[i]["key"]
+ vadsegments = res[i]["value"]
+ input_i = data_list[i]
+ speech = load_audio(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
+ speech_lengths = len(speech)
+ n = len(vadsegments)
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+ results_sorted = []
+
+ if not len(sorted_data):
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
+
+ # if kwargs["device"] == "cpu":
+ # batch_size = 0
+ if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+ batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
+
+ batch_size_ms_cum = 0
+ beg_idx = 0
+ beg_asr_total = time.time()
+ time_speech_total_per_sample = speech_lengths/16000
+ time_speech_total_all_samples += time_speech_total_per_sample
+
+ for j, _ in enumerate(range(0, n)):
+ batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
+ if j < n - 1 and (
+ batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
+ sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
+ continue
+ batch_size_ms_cum = 0
+ end_idx = j + 1
+ speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
+ beg_idx = end_idx
+
+ results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+
+ if len(results) < 1:
+ continue
+ results_sorted.extend(results)
+
+
+ pbar_total.update(1)
+ end_asr_total = time.time()
+ time_escape_total_per_sample = end_asr_total - beg_asr_total
+ pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
+ f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+
+ restored_data = [0] * n
+ for j in range(n):
+ index = sorted_data[j][1]
+ restored_data[index] = results_sorted[j]
+ result = {}
+
+ for j in range(n):
+ for k, v in restored_data[j].items():
+ if not k.startswith("timestamp"):
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += restored_data[j][k]
+ else:
+ result[k] = []
+ for t in restored_data[j][k]:
+ t[0] += vadsegments[j][0]
+ t[1] += vadsegments[j][0]
+ result[k] += restored_data[j][k]
+
+ result["key"] = key
+ results_ret_list.append(result)
+ pbar_total.update(1)
+ pbar_total.update(1)
+ end_total = time.time()
+ time_escape_total_all_samples = end_total - beg_total
+ pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+ f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
+ f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
+ return results_ret_list
+
if __name__ == '__main__':
main_hydra()
\ No newline at end of file
--
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