From fb176404cfeb40c053f4f42d01eb45c185d21ce2 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 08 一月 2024 16:20:45 +0800
Subject: [PATCH] funasr1.0 emotion2vec
---
funasr/bin/inference.py | 350 +++++++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 309 insertions(+), 41 deletions(-)
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index fd884cd..5b58907 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -4,21 +4,24 @@
import numpy as np
import hydra
import json
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig, OmegaConf, ListConfig
import logging
from funasr.download.download_from_hub import download_model
from funasr.train_utils.set_all_random_seed import set_all_random_seed
-from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_bytes
+from funasr.utils.load_utils import load_bytes
from funasr.train_utils.device_funcs import to_device
from tqdm import tqdm
from funasr.train_utils.load_pretrained_model import load_pretrained_model
import time
import random
import string
-from funasr.utils.register import registry_tables
+from funasr.register import tables
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.vad_utils import slice_padding_audio_samples
+from funasr.utils.timestamp_tools import time_stamp_sentence
-def build_iter_for_infer(data_in, input_len=None, data_type="sound"):
+def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
"""
:param input:
@@ -45,7 +48,7 @@
data = lines["source"]
key = data["key"] if "key" in data else key
else: # filelist, wav.scp, text.txt: id \t data or data
- lines = line.strip().split()
+ lines = line.strip().split(maxsplit=1)
data = lines[1] if len(lines)>1 else lines[0]
key = lines[0] if len(lines)>1 else key
@@ -55,33 +58,82 @@
key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
data_list = [data_in]
key_list = [key]
- elif isinstance(data_in, (list, tuple)): # [audio sample point, fbank]
- data_list = data_in
- key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
+ elif isinstance(data_in, (list, tuple)):
+ if data_type is not None and isinstance(data_type, (list, tuple)):
+ data_list_tmp = []
+ for data_in_i, data_type_i in zip(data_in, data_type):
+ key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
+ data_list_tmp.append(data_list_i)
+ data_list = []
+ for item in zip(*data_list_tmp):
+ data_list.append(item)
+ else:
+ # [audio sample point, fbank]
+ data_list = data_in
+ key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
else: # raw text; audio sample point, fbank; bytes
if isinstance(data_in, bytes): # audio bytes
data_in = load_bytes(data_in)
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ if key is None:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
data_list = [data_in]
key_list = [key]
return key_list, data_list
@hydra.main(config_name=None, version_base=None)
-def main_hydra(kwargs: DictConfig):
+def main_hydra(cfg: DictConfig):
+ def to_plain_list(cfg_item):
+ if isinstance(cfg_item, ListConfig):
+ return OmegaConf.to_container(cfg_item, resolve=True)
+ elif isinstance(cfg_item, DictConfig):
+ return {k: to_plain_list(v) for k, v in cfg_item.items()}
+ else:
+ return cfg_item
+
+ kwargs = to_plain_list(cfg)
log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
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):
- registry_tables.print()
+ 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")))
@@ -90,33 +142,36 @@
set_all_random_seed(kwargs.get("seed", 0))
device = kwargs.get("device", "cuda")
- if not torch.cuda.is_available() or kwargs.get("ngpu", 1):
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
device = "cpu"
- kwargs["batch_size"] = 1
+ # kwargs["batch_size"] = 1
kwargs["device"] = device
-
+
+ if kwargs.get("ncpu", None):
+ torch.set_num_threads(kwargs.get("ncpu"))
+
# build tokenizer
tokenizer = kwargs.get("tokenizer", None)
if tokenizer is not None:
- tokenizer_class = registry_tables.tokenizer_classes.get(tokenizer.lower())
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer.lower())
tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
kwargs["tokenizer"] = tokenizer
+ kwargs["token_list"] = tokenizer.token_list
# build frontend
frontend = kwargs.get("frontend", None)
if frontend is not None:
- frontend_class = registry_tables.frontend_classes.get(frontend.lower())
+ frontend_class = tables.frontend_classes.get(frontend.lower())
frontend = frontend_class(**kwargs["frontend_conf"])
kwargs["frontend"] = frontend
kwargs["input_size"] = frontend.output_size()
# build model
- model_class = registry_tables.model_classes.get(kwargs["model"].lower())
- model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
+ 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.eval()
model.to(device)
-
- kwargs["token_list"] = tokenizer.token_list
# init_param
init_param = kwargs.get("init_param", None)
@@ -128,52 +183,265 @@
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
-
- 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
- key_list, data_list = build_iter_for_infer(input, input_len=input_len, data_type=data_type)
+ return model, kwargs
+
+ 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, key=None, **cfg):
+ # import pdb; pdb.set_trace()
+ 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 = prepare_data_iterator(input, input_len=input_len, data_type=data_type, key=key)
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]
key_batch = key_list[beg_idx:end_idx]
batch = {"data_in": data_batch, "key": key_batch}
if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
- batch["data_batch"] = data_batch[0]
+ batch["data_in"] = data_batch[0]
batch["data_lengths"] = input_len
time1 = time.perf_counter()
- results, meta_data = self.model.generate(**batch, **self.kwargs)
+ with torch.no_grad():
+ 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
+ kwargs.update(cfg)
+ 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 = prepare_data_iterator(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_text_image_video(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)
+
+ # step.3 compute punc model
+ model = self.punc_model
+ kwargs = self.punc_kwargs
+ kwargs.update(cfg)
+
+ for i, result in enumerate(results_ret_list):
+ beg_punc = time.time()
+ res = self.generate(result["text"], model=model, kwargs=kwargs, **cfg)
+ end_punc = time.time()
+ print(f"time punc: {end_punc - beg_punc:0.3f}")
+
+ # sentences = time_stamp_sentence(model.punc_list, model.sentence_end_id, results_ret_list[i]["timestamp"], res[i]["text"])
+ # results_ret_list[i]["time_stamp"] = res[0]["text_postprocessed_punc"]
+ # results_ret_list[i]["sentences"] = sentences
+ results_ret_list[i]["text_with_punc"] = res[i]["text"]
+
+ 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
+
+
+class AutoFrontend:
+ def __init__(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")))
+ kwargs = download_model(**kwargs)
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend.lower())
+ frontend = frontend_class(**kwargs["frontend_conf"])
+
+ self.frontend = frontend
+ self.kwargs = kwargs
+
+ def __call__(self, input, input_len=None, kwargs=None, **cfg):
+
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len)
+ batch_size = kwargs.get("batch_size", 1)
+ device = kwargs.get("device", "cpu")
+ if device == "cpu":
+ batch_size = 1
+
+ meta_data = {}
+
+ result_list = []
+ num_samples = len(data_list)
+ pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
+
+ time0 = time.perf_counter()
+ 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]
+ key_batch = key_list[beg_idx:end_idx]
+
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_batch, 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
+
+ speech.to(device=device), speech_lengths.to(device=device)
+ batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
+ result_list.append(batch)
+
+ pbar.update(1)
+ description = (
+ f"{meta_data}, "
+ )
+ pbar.set_description(description)
+
+ time_end = time.perf_counter()
+ pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
+
+ return result_list
+
if __name__ == '__main__':
main_hydra()
\ No newline at end of file
--
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