Auto backup: 2026-02-21 07:01

.venvs/transcribe/lib/python3.11/site-packages/vosk/transcriber/cli.py

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+#!/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()

.venvs/transcribe/lib/python3.11/site-packages/vosk/transcriber/transcriber.py

@@ -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))