Initial commit: Linux Transcriber app with multi-language and auto-detection support
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Jean
275
venv/lib/python3.12/site-packages/vosk/__init__.py
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275
venv/lib/python3.12/site-packages/vosk/__init__.py
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import os
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import sys
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import srt
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import datetime
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import json
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import requests
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from urllib.request import urlretrieve
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from zipfile import ZipFile
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from re import match
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from pathlib import Path
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from .vosk_cffi import ffi as _ffi
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from tqdm import tqdm
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# Remote location of the models and local folders
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MODEL_PRE_URL = "https://alphacephei.com/vosk/models/"
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MODEL_LIST_URL = MODEL_PRE_URL + "model-list.json"
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MODEL_DIRS = [os.getenv("VOSK_MODEL_PATH"), Path("/usr/share/vosk"),
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Path.home() / "AppData/Local/vosk", Path.home() / ".cache/vosk"]
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def open_dll():
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dlldir = os.path.abspath(os.path.dirname(__file__))
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if sys.platform == "win32":
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# We want to load dependencies too
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os.environ["PATH"] = dlldir + os.pathsep + os.environ["PATH"]
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if hasattr(os, "add_dll_directory"):
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os.add_dll_directory(dlldir)
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return _ffi.dlopen(os.path.join(dlldir, "libvosk.dll"))
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elif sys.platform == "linux":
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return _ffi.dlopen(os.path.join(dlldir, "libvosk.so"))
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elif sys.platform == "darwin":
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return _ffi.dlopen(os.path.join(dlldir, "libvosk.dyld"))
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else:
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raise TypeError("Unsupported platform")
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_c = open_dll()
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def list_models():
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response = requests.get(MODEL_LIST_URL, timeout=10)
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for model in response.json():
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print(model["name"])
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def list_languages():
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response = requests.get(MODEL_LIST_URL, timeout=10)
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languages = {m["lang"] for m in response.json()}
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for lang in languages:
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print (lang)
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class Model:
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def __init__(self, model_path=None, model_name=None, lang=None):
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if model_path is not None:
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self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
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else:
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model_path = self.get_model_path(model_name, lang)
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self._handle = _c.vosk_model_new(model_path.encode("utf-8"))
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if self._handle == _ffi.NULL:
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raise Exception("Failed to create a model")
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def __del__(self):
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_c.vosk_model_free(self._handle)
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def vosk_model_find_word(self, word):
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return _c.vosk_model_find_word(self._handle, word.encode("utf-8"))
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def get_model_path(self, model_name, lang):
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if model_name is None:
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model_path = self.get_model_by_lang(lang)
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else:
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model_path = self.get_model_by_name(model_name)
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return str(model_path)
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def get_model_by_name(self, model_name):
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for directory in MODEL_DIRS:
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if directory is None or not Path(directory).exists():
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continue
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model_file_list = os.listdir(directory)
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model_file = [model for model in model_file_list if model == model_name]
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if model_file != []:
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return Path(directory, model_file[0])
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response = requests.get(MODEL_LIST_URL, timeout=10)
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result_model = [model["name"] for model in response.json() if model["name"] == model_name]
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if result_model == []:
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print("model name %s does not exist" % (model_name))
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sys.exit(1)
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else:
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self.download_model(Path(directory, result_model[0]))
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return Path(directory, result_model[0])
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def get_model_by_lang(self, lang):
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for directory in MODEL_DIRS:
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if directory is None or not Path(directory).exists():
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continue
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model_file_list = os.listdir(directory)
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model_file = [model for model in model_file_list if
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match(r"vosk-model(-small)?-{}".format(lang), model)]
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if model_file != []:
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return Path(directory, model_file[0])
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response = requests.get(MODEL_LIST_URL, timeout=10)
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result_model = [model["name"] for model in response.json() if
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model["lang"] == lang and model["type"] == "small" and model["obsolete"] == "false"]
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if result_model == []:
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print("lang %s does not exist" % (lang))
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sys.exit(1)
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else:
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self.download_model(Path(directory, result_model[0]))
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return Path(directory, result_model[0])
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def download_model(self, model_name):
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if not (model_name.parent).exists():
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(model_name.parent).mkdir(parents=True)
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with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1,
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desc=(MODEL_PRE_URL + str(model_name.name) + ".zip").rsplit("/",
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maxsplit=1)[-1]) as t:
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reporthook = self.download_progress_hook(t)
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urlretrieve(MODEL_PRE_URL + str(model_name.name) + ".zip",
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str(model_name) + ".zip", reporthook=reporthook, data=None)
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t.total = t.n
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with ZipFile(str(model_name) + ".zip", "r") as model_ref:
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model_ref.extractall(model_name.parent)
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Path(str(model_name) + ".zip").unlink()
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def download_progress_hook(self, t):
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last_b = [0]
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def update_to(b=1, bsize=1, tsize=None):
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if tsize not in (None, -1):
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t.total = tsize
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displayed = t.update((b - last_b[0]) * bsize)
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last_b[0] = b
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return displayed
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return update_to
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class SpkModel:
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def __init__(self, model_path):
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self._handle = _c.vosk_spk_model_new(model_path.encode("utf-8"))
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if self._handle == _ffi.NULL:
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raise Exception("Failed to create a speaker model")
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def __del__(self):
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_c.vosk_spk_model_free(self._handle)
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class KaldiRecognizer:
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def __init__(self, *args):
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if len(args) == 2:
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self._handle = _c.vosk_recognizer_new(args[0]._handle, args[1])
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elif len(args) == 3 and isinstance(args[2], SpkModel):
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self._handle = _c.vosk_recognizer_new_spk(args[0]._handle,
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args[1], args[2]._handle)
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elif len(args) == 3 and isinstance(args[2], str):
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self._handle = _c.vosk_recognizer_new_grm(args[0]._handle,
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args[1], args[2].encode("utf-8"))
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else:
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raise TypeError("Unknown arguments")
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if self._handle == _ffi.NULL:
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raise Exception("Failed to create a recognizer")
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def __del__(self):
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_c.vosk_recognizer_free(self._handle)
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def SetMaxAlternatives(self, max_alternatives):
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_c.vosk_recognizer_set_max_alternatives(self._handle, max_alternatives)
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def SetWords(self, enable_words):
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_c.vosk_recognizer_set_words(self._handle, 1 if enable_words else 0)
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def SetPartialWords(self, enable_partial_words):
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_c.vosk_recognizer_set_partial_words(self._handle, 1 if enable_partial_words else 0)
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def SetNLSML(self, enable_nlsml):
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_c.vosk_recognizer_set_nlsml(self._handle, 1 if enable_nlsml else 0)
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def SetSpkModel(self, spk_model):
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_c.vosk_recognizer_set_spk_model(self._handle, spk_model._handle)
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def SetGrammar(self, grammar):
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_c.vosk_recognizer_set_grm(self._handle, grammar.encode("utf-8"))
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def AcceptWaveform(self, data):
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res = _c.vosk_recognizer_accept_waveform(self._handle, data, len(data))
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if res < 0:
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raise Exception("Failed to process waveform")
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return res
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def Result(self):
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return _ffi.string(_c.vosk_recognizer_result(self._handle)).decode("utf-8")
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def PartialResult(self):
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return _ffi.string(_c.vosk_recognizer_partial_result(self._handle)).decode("utf-8")
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def FinalResult(self):
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return _ffi.string(_c.vosk_recognizer_final_result(self._handle)).decode("utf-8")
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def Reset(self):
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return _c.vosk_recognizer_reset(self._handle)
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def SrtResult(self, stream, words_per_line = 7):
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results = []
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while True:
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data = stream.read(4000)
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if len(data) == 0:
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break
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if self.AcceptWaveform(data):
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results.append(self.Result())
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results.append(self.FinalResult())
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subs = []
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for res in results:
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jres = json.loads(res)
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if not "result" in jres:
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continue
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words = jres["result"]
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for j in range(0, len(words), words_per_line):
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line = words[j : j + words_per_line]
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s = srt.Subtitle(index=len(subs),
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content=" ".join([l["word"] for l in line]),
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start=datetime.timedelta(seconds=line[0]["start"]),
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end=datetime.timedelta(seconds=line[-1]["end"]))
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subs.append(s)
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return srt.compose(subs)
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def SetLogLevel(level):
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return _c.vosk_set_log_level(level)
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def GpuInit():
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_c.vosk_gpu_init()
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def GpuThreadInit():
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_c.vosk_gpu_thread_init()
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class BatchModel:
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def __init__(self, model_path, *args):
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self._handle = _c.vosk_batch_model_new(model_path.encode('utf-8'))
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if self._handle == _ffi.NULL:
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raise Exception("Failed to create a model")
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def __del__(self):
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_c.vosk_batch_model_free(self._handle)
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def Wait(self):
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_c.vosk_batch_model_wait(self._handle)
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class BatchRecognizer:
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def __init__(self, *args):
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self._handle = _c.vosk_batch_recognizer_new(args[0]._handle, args[1])
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if self._handle == _ffi.NULL:
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raise Exception("Failed to create a recognizer")
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def __del__(self):
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_c.vosk_batch_recognizer_free(self._handle)
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def AcceptWaveform(self, data):
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res = _c.vosk_batch_recognizer_accept_waveform(self._handle, data, len(data))
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def Result(self):
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ptr = _c.vosk_batch_recognizer_front_result(self._handle)
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res = _ffi.string(ptr).decode("utf-8")
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_c.vosk_batch_recognizer_pop(self._handle)
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return res
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def FinishStream(self):
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_c.vosk_batch_recognizer_finish_stream(self._handle)
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def GetPendingChunks(self):
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return _c.vosk_batch_recognizer_get_pending_chunks(self._handle)
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