Auto backup: 2026-02-21 07:01
This commit is contained in:
Krilly
275
.venvs/transcribe/lib/python3.11/site-packages/vosk/__init__.py
Normal file
275
.venvs/transcribe/lib/python3.11/site-packages/vosk/__init__.py
Normal file
@@ -0,0 +1,275 @@
|
||||
import os
|
||||
import sys
|
||||
import srt
|
||||
import datetime
|
||||
import json
|
||||
|
||||
import requests
|
||||
from urllib.request import urlretrieve
|
||||
from zipfile import ZipFile
|
||||
from re import match
|
||||
from pathlib import Path
|
||||
from .vosk_cffi import ffi as _ffi
|
||||
from tqdm import tqdm
|
||||
|
||||
# Remote location of the models and local folders
|
||||
MODEL_PRE_URL = "https://alphacephei.com/vosk/models/"
|
||||
MODEL_LIST_URL = MODEL_PRE_URL + "model-list.json"
|
||||
MODEL_DIRS = [os.getenv("VOSK_MODEL_PATH"), Path("/usr/share/vosk"),
|
||||
Path.home() / "AppData/Local/vosk", Path.home() / ".cache/vosk"]
|
||||
|
||||
def open_dll():
|
||||
dlldir = os.path.abspath(os.path.dirname(__file__))
|
||||
if sys.platform == "win32":
|
||||
# We want to load dependencies too
|
||||
os.environ["PATH"] = dlldir + os.pathsep + os.environ["PATH"]
|
||||
if hasattr(os, "add_dll_directory"):
|
||||
os.add_dll_directory(dlldir)
|
||||
return _ffi.dlopen(os.path.join(dlldir, "libvosk.dll"))
|
||||
elif sys.platform == "linux":
|
||||
return _ffi.dlopen(os.path.join(dlldir, "libvosk.so"))
|
||||
elif sys.platform == "darwin":
|
||||
return _ffi.dlopen(os.path.join(dlldir, "libvosk.dyld"))
|
||||
else:
|
||||
raise TypeError("Unsupported platform")
|
||||
|
||||
_c = open_dll()
|
||||
|
||||
def list_models():
|
||||
response = requests.get(MODEL_LIST_URL, timeout=10)
|
||||
for model in response.json():
|
||||
print(model["name"])
|
||||
|
||||
def list_languages():
|
||||
response = requests.get(MODEL_LIST_URL, timeout=10)
|
||||
languages = {m["lang"] for m in response.json()}
|
||||
for lang in languages:
|
||||
print (lang)
|
||||
|
||||
class Model:
|
||||
def __init__(self, model_path=None, model_name=None, lang=None):
|
||||
if model_path is not None:
|
||||
self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
|
||||
else:
|
||||
model_path = self.get_model_path(model_name, lang)
|
||||
self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
|
||||
if self._handle == _ffi.NULL:
|
||||
raise Exception("Failed to create a model")
|
||||
|
||||
def __del__(self):
|
||||
_c.vosk_model_free(self._handle)
|
||||
|
||||
def vosk_model_find_word(self, word):
|
||||
return _c.vosk_model_find_word(self._handle, word.encode("utf-8"))
|
||||
|
||||
def get_model_path(self, model_name, lang):
|
||||
if model_name is None:
|
||||
model_path = self.get_model_by_lang(lang)
|
||||
else:
|
||||
model_path = self.get_model_by_name(model_name)
|
||||
return str(model_path)
|
||||
|
||||
def get_model_by_name(self, model_name):
|
||||
for directory in MODEL_DIRS:
|
||||
if directory is None or not Path(directory).exists():
|
||||
continue
|
||||
model_file_list = os.listdir(directory)
|
||||
model_file = [model for model in model_file_list if model == model_name]
|
||||
if model_file != []:
|
||||
return Path(directory, model_file[0])
|
||||
response = requests.get(MODEL_LIST_URL, timeout=10)
|
||||
result_model = [model["name"] for model in response.json() if model["name"] == model_name]
|
||||
if result_model == []:
|
||||
print("model name %s does not exist" % (model_name))
|
||||
sys.exit(1)
|
||||
else:
|
||||
self.download_model(Path(directory, result_model[0]))
|
||||
return Path(directory, result_model[0])
|
||||
|
||||
def get_model_by_lang(self, lang):
|
||||
for directory in MODEL_DIRS:
|
||||
if directory is None or not Path(directory).exists():
|
||||
continue
|
||||
model_file_list = os.listdir(directory)
|
||||
model_file = [model for model in model_file_list if
|
||||
match(r"vosk-model(-small)?-{}".format(lang), model)]
|
||||
if model_file != []:
|
||||
return Path(directory, model_file[0])
|
||||
response = requests.get(MODEL_LIST_URL, timeout=10)
|
||||
result_model = [model["name"] for model in response.json() if
|
||||
model["lang"] == lang and model["type"] == "small" and model["obsolete"] == "false"]
|
||||
if result_model == []:
|
||||
print("lang %s does not exist" % (lang))
|
||||
sys.exit(1)
|
||||
else:
|
||||
self.download_model(Path(directory, result_model[0]))
|
||||
return Path(directory, result_model[0])
|
||||
|
||||
def download_model(self, model_name):
|
||||
if not (model_name.parent).exists():
|
||||
(model_name.parent).mkdir(parents=True)
|
||||
with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1,
|
||||
desc=(MODEL_PRE_URL + str(model_name.name) + ".zip").rsplit("/",
|
||||
maxsplit=1)[-1]) as t:
|
||||
reporthook = self.download_progress_hook(t)
|
||||
urlretrieve(MODEL_PRE_URL + str(model_name.name) + ".zip",
|
||||
str(model_name) + ".zip", reporthook=reporthook, data=None)
|
||||
t.total = t.n
|
||||
with ZipFile(str(model_name) + ".zip", "r") as model_ref:
|
||||
model_ref.extractall(model_name.parent)
|
||||
Path(str(model_name) + ".zip").unlink()
|
||||
|
||||
def download_progress_hook(self, t):
|
||||
last_b = [0]
|
||||
def update_to(b=1, bsize=1, tsize=None):
|
||||
if tsize not in (None, -1):
|
||||
t.total = tsize
|
||||
displayed = t.update((b - last_b[0]) * bsize)
|
||||
last_b[0] = b
|
||||
return displayed
|
||||
return update_to
|
||||
|
||||
class SpkModel:
|
||||
|
||||
def __init__(self, model_path):
|
||||
self._handle = _c.vosk_spk_model_new(model_path.encode("utf-8"))
|
||||
|
||||
if self._handle == _ffi.NULL:
|
||||
raise Exception("Failed to create a speaker model")
|
||||
|
||||
def __del__(self):
|
||||
_c.vosk_spk_model_free(self._handle)
|
||||
|
||||
class KaldiRecognizer:
|
||||
|
||||
def __init__(self, *args):
|
||||
if len(args) == 2:
|
||||
self._handle = _c.vosk_recognizer_new(args[0]._handle, args[1])
|
||||
elif len(args) == 3 and isinstance(args[2], SpkModel):
|
||||
self._handle = _c.vosk_recognizer_new_spk(args[0]._handle,
|
||||
args[1], args[2]._handle)
|
||||
elif len(args) == 3 and isinstance(args[2], str):
|
||||
self._handle = _c.vosk_recognizer_new_grm(args[0]._handle,
|
||||
args[1], args[2].encode("utf-8"))
|
||||
else:
|
||||
raise TypeError("Unknown arguments")
|
||||
|
||||
if self._handle == _ffi.NULL:
|
||||
raise Exception("Failed to create a recognizer")
|
||||
|
||||
def __del__(self):
|
||||
_c.vosk_recognizer_free(self._handle)
|
||||
|
||||
def SetMaxAlternatives(self, max_alternatives):
|
||||
_c.vosk_recognizer_set_max_alternatives(self._handle, max_alternatives)
|
||||
|
||||
def SetWords(self, enable_words):
|
||||
_c.vosk_recognizer_set_words(self._handle, 1 if enable_words else 0)
|
||||
|
||||
def SetPartialWords(self, enable_partial_words):
|
||||
_c.vosk_recognizer_set_partial_words(self._handle, 1 if enable_partial_words else 0)
|
||||
|
||||
def SetNLSML(self, enable_nlsml):
|
||||
_c.vosk_recognizer_set_nlsml(self._handle, 1 if enable_nlsml else 0)
|
||||
|
||||
def SetSpkModel(self, spk_model):
|
||||
_c.vosk_recognizer_set_spk_model(self._handle, spk_model._handle)
|
||||
|
||||
def SetGrammar(self, grammar):
|
||||
_c.vosk_recognizer_set_grm(self._handle, grammar.encode("utf-8"))
|
||||
|
||||
def AcceptWaveform(self, data):
|
||||
res = _c.vosk_recognizer_accept_waveform(self._handle, data, len(data))
|
||||
if res < 0:
|
||||
raise Exception("Failed to process waveform")
|
||||
return res
|
||||
|
||||
def Result(self):
|
||||
return _ffi.string(_c.vosk_recognizer_result(self._handle)).decode("utf-8")
|
||||
|
||||
def PartialResult(self):
|
||||
return _ffi.string(_c.vosk_recognizer_partial_result(self._handle)).decode("utf-8")
|
||||
|
||||
def FinalResult(self):
|
||||
return _ffi.string(_c.vosk_recognizer_final_result(self._handle)).decode("utf-8")
|
||||
|
||||
def Reset(self):
|
||||
return _c.vosk_recognizer_reset(self._handle)
|
||||
|
||||
def SrtResult(self, stream, words_per_line = 7):
|
||||
results = []
|
||||
|
||||
while True:
|
||||
data = stream.read(4000)
|
||||
if len(data) == 0:
|
||||
break
|
||||
if self.AcceptWaveform(data):
|
||||
results.append(self.Result())
|
||||
results.append(self.FinalResult())
|
||||
|
||||
subs = []
|
||||
for res in results:
|
||||
jres = json.loads(res)
|
||||
if not "result" in jres:
|
||||
continue
|
||||
words = jres["result"]
|
||||
for j in range(0, len(words), words_per_line):
|
||||
line = words[j : j + words_per_line]
|
||||
s = srt.Subtitle(index=len(subs),
|
||||
content=" ".join([l["word"] for l in line]),
|
||||
start=datetime.timedelta(seconds=line[0]["start"]),
|
||||
end=datetime.timedelta(seconds=line[-1]["end"]))
|
||||
subs.append(s)
|
||||
|
||||
return srt.compose(subs)
|
||||
|
||||
def SetLogLevel(level):
|
||||
return _c.vosk_set_log_level(level)
|
||||
|
||||
|
||||
def GpuInit():
|
||||
_c.vosk_gpu_init()
|
||||
|
||||
|
||||
def GpuThreadInit():
|
||||
_c.vosk_gpu_thread_init()
|
||||
|
||||
class BatchModel:
|
||||
|
||||
def __init__(self, model_path, *args):
|
||||
self._handle = _c.vosk_batch_model_new(model_path.encode('utf-8'))
|
||||
|
||||
if self._handle == _ffi.NULL:
|
||||
raise Exception("Failed to create a model")
|
||||
|
||||
def __del__(self):
|
||||
_c.vosk_batch_model_free(self._handle)
|
||||
|
||||
def Wait(self):
|
||||
_c.vosk_batch_model_wait(self._handle)
|
||||
|
||||
class BatchRecognizer:
|
||||
|
||||
def __init__(self, *args):
|
||||
self._handle = _c.vosk_batch_recognizer_new(args[0]._handle, args[1])
|
||||
|
||||
if self._handle == _ffi.NULL:
|
||||
raise Exception("Failed to create a recognizer")
|
||||
|
||||
def __del__(self):
|
||||
_c.vosk_batch_recognizer_free(self._handle)
|
||||
|
||||
def AcceptWaveform(self, data):
|
||||
res = _c.vosk_batch_recognizer_accept_waveform(self._handle, data, len(data))
|
||||
|
||||
def Result(self):
|
||||
ptr = _c.vosk_batch_recognizer_front_result(self._handle)
|
||||
res = _ffi.string(ptr).decode("utf-8")
|
||||
_c.vosk_batch_recognizer_pop(self._handle)
|
||||
return res
|
||||
|
||||
def FinishStream(self):
|
||||
_c.vosk_batch_recognizer_finish_stream(self._handle)
|
||||
|
||||
def GetPendingChunks(self):
|
||||
return _c.vosk_batch_recognizer_get_pending_chunks(self._handle)
|
||||
Binary file not shown.
Binary file not shown.
BIN
.venvs/transcribe/lib/python3.11/site-packages/vosk/libvosk.so
Executable file
BIN
.venvs/transcribe/lib/python3.11/site-packages/vosk/libvosk.so
Executable file
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,89 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
import os
|
||||
|
||||
from pathlib import Path
|
||||
from vosk import list_models, list_languages
|
||||
from vosk.transcriber.transcriber import Transcriber
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description = "Transcribe audio file and save result in selected format")
|
||||
parser.add_argument(
|
||||
"--model", "-m", type=str,
|
||||
help="model path")
|
||||
parser.add_argument(
|
||||
"--server", "-s", const="ws://localhost:2700", action="store_const",
|
||||
help="use server for recognition")
|
||||
parser.add_argument(
|
||||
"--list-models", default=False, action="store_true",
|
||||
help="list available models")
|
||||
parser.add_argument(
|
||||
"--list-languages", default=False, action="store_true",
|
||||
help="list available languages")
|
||||
parser.add_argument(
|
||||
"--model-name", "-n", type=str,
|
||||
help="select model by name")
|
||||
parser.add_argument(
|
||||
"--lang", "-l", default="en-us", type=str,
|
||||
help="select model by language")
|
||||
parser.add_argument(
|
||||
"--input", "-i", type=str,
|
||||
help="audiofile")
|
||||
parser.add_argument(
|
||||
"--output", "-o", default="", type=str,
|
||||
help="optional output filename path")
|
||||
parser.add_argument(
|
||||
"--output-type", "-t", default="txt", type=str,
|
||||
help="optional arg output data type")
|
||||
parser.add_argument(
|
||||
"--tasks", "-ts", default=10, type=int,
|
||||
help="number of parallel recognition tasks")
|
||||
parser.add_argument(
|
||||
"--log-level", default="INFO",
|
||||
help="logging level")
|
||||
|
||||
def main():
|
||||
|
||||
args = parser.parse_args()
|
||||
log_level = args.log_level.upper()
|
||||
logging.getLogger().setLevel(log_level)
|
||||
|
||||
if args.list_models is True:
|
||||
list_models()
|
||||
return
|
||||
|
||||
if args.list_languages is True:
|
||||
list_languages()
|
||||
return
|
||||
|
||||
if not args.input:
|
||||
logging.info("Please specify input file or directory")
|
||||
sys.exit(1)
|
||||
|
||||
if not Path(args.input).exists():
|
||||
logging.info("File/folder {args.input} does not exist, "\
|
||||
"please specify an existing file/directory")
|
||||
sys.exit(1)
|
||||
|
||||
transcriber = Transcriber(args)
|
||||
|
||||
if Path(args.input).is_dir():
|
||||
task_list = [(Path(args.input, fn),
|
||||
Path(args.output,
|
||||
Path(fn).stem).with_suffix("." + args.output_type)) for fn in os.listdir(args.input)]
|
||||
elif Path(args.input).is_file():
|
||||
if args.output == "":
|
||||
task_list = [(Path(args.input), args.output)]
|
||||
else:
|
||||
task_list = [(Path(args.input), Path(args.output))]
|
||||
else:
|
||||
logging.info("Wrong arguments")
|
||||
sys.exit(1)
|
||||
|
||||
transcriber.process_task_list(task_list)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,195 @@
|
||||
import json
|
||||
import logging
|
||||
import asyncio
|
||||
import websockets
|
||||
import srt
|
||||
import datetime
|
||||
import shlex
|
||||
import subprocess
|
||||
|
||||
from vosk import KaldiRecognizer, Model
|
||||
from queue import Queue
|
||||
from timeit import default_timer as timer
|
||||
from multiprocessing.dummy import Pool
|
||||
|
||||
CHUNK_SIZE = 4000
|
||||
SAMPLE_RATE = 16000.0
|
||||
|
||||
class Transcriber:
|
||||
|
||||
def __init__(self, args):
|
||||
self.model = Model(model_path=args.model, model_name=args.model_name, lang=args.lang)
|
||||
self.args = args
|
||||
self.queue = Queue()
|
||||
|
||||
def recognize_stream(self, rec, stream):
|
||||
tot_samples = 0
|
||||
result = []
|
||||
|
||||
while True:
|
||||
data = stream.stdout.read(CHUNK_SIZE)
|
||||
|
||||
if len(data) == 0:
|
||||
break
|
||||
|
||||
tot_samples += len(data)
|
||||
if rec.AcceptWaveform(data):
|
||||
jres = json.loads(rec.Result())
|
||||
logging.info(jres)
|
||||
result.append(jres)
|
||||
else:
|
||||
jres = json.loads(rec.PartialResult())
|
||||
if jres["partial"] != "":
|
||||
logging.info(jres)
|
||||
|
||||
jres = json.loads(rec.FinalResult())
|
||||
result.append(jres)
|
||||
|
||||
return result, tot_samples
|
||||
|
||||
async def recognize_stream_server(self, proc):
|
||||
async with websockets.connect(self.args.server) as websocket:
|
||||
tot_samples = 0
|
||||
result = []
|
||||
|
||||
await websocket.send('{ "config" : { "sample_rate" : %f } }' % (SAMPLE_RATE))
|
||||
while True:
|
||||
data = await proc.stdout.read(CHUNK_SIZE)
|
||||
tot_samples += len(data)
|
||||
if len(data) == 0:
|
||||
break
|
||||
await websocket.send(data)
|
||||
jres = json.loads(await websocket.recv())
|
||||
logging.info(jres)
|
||||
if not "partial" in jres:
|
||||
result.append(jres)
|
||||
await websocket.send('{"eof" : 1}')
|
||||
jres = json.loads(await websocket.recv())
|
||||
logging.info(jres)
|
||||
result.append(jres)
|
||||
|
||||
return result, tot_samples
|
||||
|
||||
|
||||
def format_result(self, result, words_per_line=7):
|
||||
processed_result = ""
|
||||
if self.args.output_type == "srt":
|
||||
subs = []
|
||||
|
||||
for _, res in enumerate(result):
|
||||
if not "result" in res:
|
||||
continue
|
||||
words = res["result"]
|
||||
|
||||
for j in range(0, len(words), words_per_line):
|
||||
line = words[j : j + words_per_line]
|
||||
s = srt.Subtitle(index=len(subs),
|
||||
content = " ".join([l["word"] for l in line]),
|
||||
start=datetime.timedelta(seconds=line[0]["start"]),
|
||||
end=datetime.timedelta(seconds=line[-1]["end"]))
|
||||
subs.append(s)
|
||||
processed_result = srt.compose(subs)
|
||||
|
||||
elif self.args.output_type == "txt":
|
||||
for part in result:
|
||||
if part["text"] != "":
|
||||
processed_result += part["text"] + "\n"
|
||||
|
||||
elif self.args.output_type == "json":
|
||||
monologues = {"schemaVersion":"2.0", "monologues":[], "text":[]}
|
||||
for part in result:
|
||||
if part["text"] != "":
|
||||
monologue["text"] += part["text"]
|
||||
for _, res in enumerate(result):
|
||||
if not "result" in res:
|
||||
continue
|
||||
monologue = { "speaker": {"id": "unknown", "name": None}, "start": 0, "end": 0, "terms": []}
|
||||
monologue["start"] = res["result"][0]["start"]
|
||||
monologue["end"] = res["result"][-1]["end"]
|
||||
monologue["terms"] = [{"confidence": t["conf"], "start": t["start"], "end": t["end"], "text": t["word"], "type": "WORD" } for t in res["result"]]
|
||||
monologues["monologues"].append(monologue)
|
||||
processed_result = json.dumps(monologues)
|
||||
return processed_result
|
||||
|
||||
def resample_ffmpeg(self, infile):
|
||||
cmd = shlex.split("ffmpeg -nostdin -loglevel quiet "
|
||||
"-i \'{}\' -ar {} -ac 1 -f s16le -".format(str(infile), SAMPLE_RATE))
|
||||
stream = subprocess.Popen(cmd, stdout=subprocess.PIPE)
|
||||
return stream
|
||||
|
||||
async def resample_ffmpeg_async(self, infile):
|
||||
cmd = "ffmpeg -nostdin -loglevel quiet "\
|
||||
"-i \'{}\' -ar {} -ac 1 -f s16le -".format(str(infile), SAMPLE_RATE)
|
||||
return await asyncio.create_subprocess_shell(cmd, stdout=subprocess.PIPE)
|
||||
|
||||
async def server_worker(self):
|
||||
while True:
|
||||
try:
|
||||
input_file, output_file = self.queue.get_nowait()
|
||||
except Exception:
|
||||
break
|
||||
|
||||
logging.info("Recognizing {}".format(input_file))
|
||||
start_time = timer()
|
||||
proc = await self.resample_ffmpeg_async(input_file)
|
||||
result, tot_samples = await self.recognize_stream_server(proc)
|
||||
|
||||
processed_result = self.format_result(result)
|
||||
if output_file != "":
|
||||
logging.info("File {} processing complete".format(output_file))
|
||||
with open(output_file, "w", encoding="utf-8") as fh:
|
||||
fh.write(processed_result)
|
||||
else:
|
||||
print(processed_result)
|
||||
|
||||
await proc.wait()
|
||||
|
||||
elapsed = timer() - start_time
|
||||
logging.info("Execution time: {:.3f} sec; "\
|
||||
"xRT {:.3f}".format(elapsed, float(elapsed) * (2 * SAMPLE_RATE) / tot_samples))
|
||||
self.queue.task_done()
|
||||
|
||||
def pool_worker(self, inputdata):
|
||||
logging.info("Recognizing {}".format(inputdata[0]))
|
||||
start_time = timer()
|
||||
|
||||
try:
|
||||
stream = self.resample_ffmpeg(inputdata[0])
|
||||
except FileNotFoundError as e:
|
||||
print(e, "Missing FFMPEG, please install and try again")
|
||||
return
|
||||
except Exception as e:
|
||||
logging.info(e)
|
||||
return
|
||||
|
||||
rec = KaldiRecognizer(self.model, SAMPLE_RATE)
|
||||
rec.SetWords(True)
|
||||
result, tot_samples = self.recognize_stream(rec, stream)
|
||||
processed_result = self.format_result(result)
|
||||
|
||||
if inputdata[1] != "":
|
||||
logging.info("File {} processing complete".format(inputdata[1]))
|
||||
with open(inputdata[1], "w", encoding="utf-8") as fh:
|
||||
fh.write(processed_result)
|
||||
else:
|
||||
print(processed_result)
|
||||
|
||||
elapsed = timer() - start_time
|
||||
logging.info("Execution time: {:.3f} sec; "\
|
||||
"xRT {:.3f}".format(elapsed, float(elapsed) * (2 * SAMPLE_RATE) / tot_samples))
|
||||
|
||||
async def process_task_list_server(self, task_list):
|
||||
for x in task_list:
|
||||
self.queue.put(x)
|
||||
workers = [asyncio.create_task(self.server_worker()) for i in range(self.args.tasks)]
|
||||
await asyncio.gather(*workers)
|
||||
|
||||
def process_task_list_pool(self, task_list):
|
||||
with Pool() as pool:
|
||||
pool.map(self.pool_worker, task_list)
|
||||
|
||||
def process_task_list(self, task_list):
|
||||
if self.args.server is None:
|
||||
self.process_task_list_pool(task_list)
|
||||
else:
|
||||
asyncio.run(self.process_task_list_server(task_list))
|
||||
@@ -0,0 +1,10 @@
|
||||
# auto-generated file
|
||||
import _cffi_backend
|
||||
|
||||
ffi = _cffi_backend.FFI('vosk.vosk_cffi',
|
||||
_version = 0x2601,
|
||||
_types = b'\x00\x00\x04\x0D\x00\x00\x65\x03\x00\x00\x00\x0F\x00\x00\x1C\x0D\x00\x00\x60\x03\x00\x00\x0D\x01\x00\x00\x00\x0F\x00\x00\x0B\x0D\x00\x00\x01\x11\x00\x00\x00\x0F\x00\x00\x1F\x0D\x00\x00\x62\x03\x00\x00\x0D\x01\x00\x00\x00\x0F\x00\x00\x1F\x0D\x00\x00\x0B\x11\x00\x00\x0D\x01\x00\x00\x64\x03\x00\x00\x00\x0F\x00\x00\x1F\x0D\x00\x00\x0B\x11\x00\x00\x0D\x01\x00\x00\x01\x11\x00\x00\x00\x0F\x00\x00\x11\x0D\x00\x00\x01\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x61\x03\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x63\x03\x00\x00\x00\x0F\x00\x00\x2B\x0D\x00\x00\x1C\x11\x00\x00\x00\x0F\x00\x00\x2B\x0D\x00\x00\x0B\x11\x00\x00\x01\x11\x00\x00\x00\x0F\x00\x00\x2B\x0D\x00\x00\x1F\x11\x00\x00\x01\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x2B\x0D\x00\x00\x1F\x11\x00\x00\x05\x03\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x2B\x0D\x00\x00\x1F\x11\x00\x00\x66\x03\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x04\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1C\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1C\x11\x00\x00\x01\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1C\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x0B\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1F\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1F\x11\x00\x00\x11\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1F\x11\x00\x00\x01\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x1F\x11\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x11\x11\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x67\x0D\x00\x00\x00\x0F\x00\x00\x00\x09\x00\x00\x01\x09\x00\x00\x02\x09\x00\x00\x03\x09\x00\x00\x04\x09\x00\x00\x02\x01\x00\x00\x05\x01\x00\x00\x00\x01',
|
||||
_globals = (b'\x00\x00\x37\x23vosk_batch_model_free',0,b'\x00\x00\x00\x23vosk_batch_model_new',0,b'\x00\x00\x37\x23vosk_batch_model_wait',0,b'\x00\x00\x3D\x23vosk_batch_recognizer_accept_waveform',0,b'\x00\x00\x3A\x23vosk_batch_recognizer_finish_stream',0,b'\x00\x00\x3A\x23vosk_batch_recognizer_free',0,b'\x00\x00\x1B\x23vosk_batch_recognizer_front_result',0,b'\x00\x00\x21\x23vosk_batch_recognizer_get_pending_chunks',0,b'\x00\x00\x03\x23vosk_batch_recognizer_new',0,b'\x00\x00\x3A\x23vosk_batch_recognizer_pop',0,b'\x00\x00\x42\x23vosk_batch_recognizer_set_nlsml',0,b'\x00\x00\x5E\x23vosk_gpu_init',0,b'\x00\x00\x5E\x23vosk_gpu_thread_init',0,b'\x00\x00\x24\x23vosk_model_find_word',0,b'\x00\x00\x46\x23vosk_model_free',0,b'\x00\x00\x07\x23vosk_model_new',0,b'\x00\x00\x28\x23vosk_recognizer_accept_waveform',0,b'\x00\x00\x2D\x23vosk_recognizer_accept_waveform_f',0,b'\x00\x00\x32\x23vosk_recognizer_accept_waveform_s',0,b'\x00\x00\x1E\x23vosk_recognizer_final_result',0,b'\x00\x00\x49\x23vosk_recognizer_free',0,b'\x00\x00\x0A\x23vosk_recognizer_new',0,b'\x00\x00\x13\x23vosk_recognizer_new_grm',0,b'\x00\x00\x0E\x23vosk_recognizer_new_spk',0,b'\x00\x00\x1E\x23vosk_recognizer_partial_result',0,b'\x00\x00\x49\x23vosk_recognizer_reset',0,b'\x00\x00\x1E\x23vosk_recognizer_result',0,b'\x00\x00\x50\x23vosk_recognizer_set_grm',0,b'\x00\x00\x54\x23vosk_recognizer_set_max_alternatives',0,b'\x00\x00\x54\x23vosk_recognizer_set_nlsml',0,b'\x00\x00\x54\x23vosk_recognizer_set_partial_words',0,b'\x00\x00\x4C\x23vosk_recognizer_set_spk_model',0,b'\x00\x00\x54\x23vosk_recognizer_set_words',0,b'\x00\x00\x5B\x23vosk_set_log_level',0,b'\x00\x00\x58\x23vosk_spk_model_free',0,b'\x00\x00\x18\x23vosk_spk_model_new',0),
|
||||
_struct_unions = ((b'\x00\x00\x00\x60\x00\x00\x00\x10VoskBatchModel',),(b'\x00\x00\x00\x61\x00\x00\x00\x10VoskBatchRecognizer',),(b'\x00\x00\x00\x62\x00\x00\x00\x10VoskModel',),(b'\x00\x00\x00\x63\x00\x00\x00\x10VoskRecognizer',),(b'\x00\x00\x00\x64\x00\x00\x00\x10VoskSpkModel',)),
|
||||
_typenames = (b'\x00\x00\x00\x60VoskBatchModel',b'\x00\x00\x00\x61VoskBatchRecognizer',b'\x00\x00\x00\x62VoskModel',b'\x00\x00\x00\x63VoskRecognizer',b'\x00\x00\x00\x64VoskSpkModel'),
|
||||
)
|
||||
Reference in New Issue
Block a user