yjyoon / whisper_streaming_Deprecated star
Dominik Macháček 2023-06-02
server
@e68952aec2897b5c09bec5d690844f9656c2b816
6276797
e68952a
README.md
--- README.md
+++ README.md
@@ -110,6 +110,19 @@
 
 online.init()  # refresh if you're going to re-use the object for the next audio
 
 ```
 
 
+## Usage: Server
 
+
 
+`whisper_online_server.py` entry point has the same model option sas the entry point above, plus `--host` and `--port`, and no audio path.
 
+
 
+Client example:
 
+
 
+```
 
+arecord -f S16_LE -c1 -r 16000 -t raw -D default | nc localhost 43001
 
+```
 
+
 
+- arecord is an example program that sends audio from a sound device, in raw audio format -- 16000 sampling rate, mono channel, S16\_LE -- signed 16-bit integer low endian
 
+
 
+- nc is netcat, server host and port are e.g. localhost 430001
 
 
 
 ## Background
 
e68952a
whisper_online_server.py (added)
+++ whisper_online_server.py
@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+from whisper_online import *
+
+import sys
+import argparse
+import os
+parser = argparse.ArgumentParser()
+
+# server options
+parser.add_argument("--host", type=str, default='localhost')
+parser.add_argument("--port", type=int, default=43007)
+
+
+# options from whisper_online
+# TODO: code repetition
+
+parser.add_argument('--min-chunk-size', type=float, default=1.0, help='Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.')
+parser.add_argument('--model', type=str, default='large-v2', choices="tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large".split(","),help="Name size of the Whisper model to use (default: large-v2). The model is automatically downloaded from the model hub if not present in model cache dir.")
+parser.add_argument('--model_cache_dir', type=str, default=None, help="Overriding the default model cache dir where models downloaded from the hub are saved")
+parser.add_argument('--model_dir', type=str, default=None, help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.")
+parser.add_argument('--lan', '--language', type=str, default='en', help="Language code for transcription, e.g. en,de,cs.")
+parser.add_argument('--task', type=str, default='transcribe', choices=["transcribe","translate"],help="Transcribe or translate.")
+parser.add_argument('--start_at', type=float, default=0.0, help='Start processing audio at this time.')
+parser.add_argument('--backend', type=str, default="faster-whisper", choices=["faster-whisper", "whisper_timestamped"],help='Load only this backend for Whisper processing.')
+parser.add_argument('--offline', action="store_true", default=False, help='Offline mode.')
+parser.add_argument('--vad', action="store_true", default=False, help='Use VAD = voice activity detection, with the default parameters.')
+args = parser.parse_args()
+
+
+# setting whisper object by args 
+
+SAMPLING_RATE = 16000
+
+size = args.model
+language = args.lan
+
+t = time.time()
+print(f"Loading Whisper {size} model for {language}...",file=sys.stderr,end=" ",flush=True)
+
+if args.backend == "faster-whisper":
+    from faster_whisper import WhisperModel
+    asr_cls = FasterWhisperASR
+else:
+    import whisper
+    import whisper_timestamped
+#    from whisper_timestamped_model import WhisperTimestampedASR
+    asr_cls = WhisperTimestampedASR
+
+asr = asr_cls(modelsize=size, lan=language, cache_dir=args.model_cache_dir, model_dir=args.model_dir)
+
+if args.task == "translate":
+    asr.set_translate_task()
+
+e = time.time()
+print(f"done. It took {round(e-t,2)} seconds.",file=sys.stderr)
+
+if args.vad:
+    print("setting VAD filter",file=sys.stderr)
+    asr.use_vad()
+
+
+min_chunk = args.min_chunk_size
+online = OnlineASRProcessor(language,asr)
+
+
+
+demo_audio_path = "cs-maji-2.16k.wav"
+if os.path.exists(demo_audio_path):
+    # load the audio into the LRU cache before we start the timer
+    a = load_audio_chunk(demo_audio_path,0,1)
+
+    # TODO: it should be tested whether it's meaningful
+    # warm up the ASR, because the very first transcribe takes much more time than the other
+    asr.transcribe(a)
+else:
+    print("Whisper is not warmed up",file=sys.stderr)
+
+
+
+
+######### Server objects
+
+import line_packet
+import socket
+
+import logging
+
+
+class Connection:
+    '''it wraps conn object'''
+    PACKET_SIZE = 65536
+
+    def __init__(self, conn):
+        self.conn = conn
+        self.last_line = ""
+
+        self.conn.setblocking(True)
+
+    def send(self, line):
+        '''it doesn't send the same line twice, because it was problematic in online-text-flow-events'''
+        if line == self.last_line:
+            return
+        line_packet.send_one_line(self.conn, line)
+        self.last_line = line
+
+    def receive_lines(self):
+        in_line = line_packet.receive_lines(self.conn)
+        return in_line
+
+    def non_blocking_receive_audio(self):
+        r = self.conn.recv(self.PACKET_SIZE)
+        return r
+
+
+import io
+import soundfile
+
+# wraps socket and ASR object, and serves one client connection. 
+# next client should be served by a new instance of this object
+class ServerProcessor:
+
+    def __init__(self, c, online_asr_proc, min_chunk):
+        self.connection = c
+        self.online_asr_proc = online_asr_proc
+        self.min_chunk = min_chunk
+
+        self.last_end = None
+
+    def receive_audio_chunk(self):
+        # receive all audio that is available by this time
+        # blocks operation if less than self.min_chunk seconds is available
+        # unblocks if connection is closed or a chunk is available
+        out = []
+        while sum(len(x) for x in out) < self.min_chunk*SAMPLING_RATE:
+            raw_bytes = self.connection.non_blocking_receive_audio()
+            print(raw_bytes[:10])
+            print(len(raw_bytes))
+            if not raw_bytes:
+                break
+            sf = soundfile.SoundFile(io.BytesIO(raw_bytes), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
+            audio, _ = librosa.load(sf,sr=SAMPLING_RATE)
+            out.append(audio)
+        if not out:
+            return None
+        return np.concatenate(out)
+
+    def format_output_transcript(self,o):
+        # output format in stdout is like:
+        # 0 1720 Takhle to je
+        # - the first two words are:
+        #    - beg and end timestamp of the text segment, as estimated by Whisper model. The timestamps are not accurate, but they're useful anyway
+        # - the next words: segment transcript
+
+        # This function differs from whisper_online.output_transcript in the following:
+        # succeeding [beg,end] intervals are not overlapping because ELITR protocol (implemented in online-text-flow events) requires it.
+        # Therefore, beg, is max of previous end and current beg outputed by Whisper.
+        # Usually it differs negligibly, by appx 20 ms.
+
+        if o[0] is not None:
+            beg, end = o[0]*1000,o[1]*1000
+            if self.last_end is not None:
+                beg = max(beg, self.last_end)
+
+            self.last_end = end
+            print("%1.0f %1.0f %s" % (beg,end,o[2]),flush=True,file=sys.stderr)
+            return "%1.0f %1.0f %s" % (beg,end,o[2])
+        else:
+            print(o,file=sys.stderr,flush=True)
+            return None
+
+    def send_result(self, o):
+        msg = self.format_output_transcript(o)
+        if msg is not None:
+            self.connection.send(msg)
+
+    def process(self):
+        # handle one client connection
+        self.online_asr_proc.init()
+        while True:
+            a = self.receive_audio_chunk()
+            if a is None:
+                print("break here",file=sys.stderr)
+                break
+            self.online_asr_proc.insert_audio_chunk(a)
+            o = online.process_iter()
+            self.send_result(o)
+#        o = online.finish()  # this should be working
+#        self.send_result(o)
+
+
+
+
+# Start logging.
+level = logging.INFO
+logging.basicConfig(level=level, format='whisper-server-%(levelname)s: %(message)s')
+
+# server loop
+
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    s.bind((args.host, args.port))
+    s.listen(1)
+    logging.info('INFO: Listening on'+str((args.host, args.port)))
+    while True:
+        conn, addr = s.accept()
+        logging.info('INFO: Connected to client on {}'.format(addr))
+        connection = Connection(conn)
+        proc = ServerProcessor(connection, online, min_chunk)
+        proc.process()
+        conn.close()
+        logging.info('INFO: Connection to client closed')
+logging.info('INFO: Connection closed, terminating.')
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