move to src

src/whisper_streaming/online_asr.py

@@ -311,7 +311,7 @@ class VACOnlineASRProcessor(OnlineASRProcessor):
         import torch
 
         model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
-        from silero_vad_iterator import FixedVADIterator
+        from src.whisper_streaming.silero_vad_iterator import FixedVADIterator
 
         self.vac = FixedVADIterator(
             model

src/whisper_streaming/silero_vad_iterator.py

@@ -0,0 +1,163 @@
+import torch
+
+# This is copied from silero-vad's vad_utils.py:
+# https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/utils_vad.py#L340
+# (except changed defaults)
+
+# Their licence is MIT, same as ours: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE
+
+
+class VADIterator:
+    def __init__(
+        self,
+        model,
+        threshold: float = 0.5,
+        sampling_rate: int = 16000,
+        min_silence_duration_ms: int = 500,  # makes sense on one recording that I checked
+        speech_pad_ms: int = 100,  # same
+    ):
+        """
+        Class for stream imitation
+
+        Parameters
+        ----------
+        model: preloaded .jit silero VAD model
+
+        threshold: float (default - 0.5)
+            Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH.
+            It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
+
+        sampling_rate: int (default - 16000)
+            Currently silero VAD models support 8000 and 16000 sample rates
+
+        min_silence_duration_ms: int (default - 100 milliseconds)
+            In the end of each speech chunk wait for min_silence_duration_ms before separating it
+
+        speech_pad_ms: int (default - 30 milliseconds)
+            Final speech chunks are padded by speech_pad_ms each side
+        """
+
+        self.model = model
+        self.threshold = threshold
+        self.sampling_rate = sampling_rate
+
+        if sampling_rate not in [8000, 16000]:
+            raise ValueError(
+                "VADIterator does not support sampling rates other than [8000, 16000]"
+            )
+
+        self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
+        self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
+        self.reset_states()
+
+    def reset_states(self):
+
+        self.model.reset_states()
+        self.triggered = False
+        self.temp_end = 0
+        self.current_sample = 0
+
+    def __call__(self, x, return_seconds=False):
+        """
+        x: torch.Tensor
+            audio chunk (see examples in repo)
+
+        return_seconds: bool (default - False)
+            whether return timestamps in seconds (default - samples)
+        """
+
+        if not torch.is_tensor(x):
+            try:
+                x = torch.Tensor(x)
+            except:
+                raise TypeError("Audio cannot be casted to tensor. Cast it manually")
+
+        window_size_samples = len(x[0]) if x.dim() == 2 else len(x)
+        self.current_sample += window_size_samples
+
+        speech_prob = self.model(x, self.sampling_rate).item()
+
+        if (speech_prob >= self.threshold) and self.temp_end:
+            self.temp_end = 0
+
+        if (speech_prob >= self.threshold) and not self.triggered:
+            self.triggered = True
+            speech_start = self.current_sample - self.speech_pad_samples
+            return {
+                "start": (
+                    int(speech_start)
+                    if not return_seconds
+                    else round(speech_start / self.sampling_rate, 1)
+                )
+            }
+
+        if (speech_prob < self.threshold - 0.15) and self.triggered:
+            if not self.temp_end:
+                self.temp_end = self.current_sample
+            if self.current_sample - self.temp_end < self.min_silence_samples:
+                return None
+            else:
+                speech_end = self.temp_end + self.speech_pad_samples
+                self.temp_end = 0
+                self.triggered = False
+                return {
+                    "end": (
+                        int(speech_end)
+                        if not return_seconds
+                        else round(speech_end / self.sampling_rate, 1)
+                    )
+                }
+
+        return None
+
+
+#######################
+# because Silero now requires exactly 512-sized audio chunks
+
+import numpy as np
+
+
+class FixedVADIterator(VADIterator):
+    """It fixes VADIterator by allowing to process any audio length, not only exactly 512 frames at once.
+    If audio to be processed at once is long and multiple voiced segments detected,
+    then __call__ returns the start of the first segment, and end (or middle, which means no end) of the last segment.
+    """
+
+    def reset_states(self):
+        super().reset_states()
+        self.buffer = np.array([], dtype=np.float32)
+
+    def __call__(self, x, return_seconds=False):
+        self.buffer = np.append(self.buffer, x)
+        ret = None
+        while len(self.buffer) >= 512:
+            r = super().__call__(self.buffer[:512], return_seconds=return_seconds)
+            self.buffer = self.buffer[512:]
+            if ret is None:
+                ret = r
+            elif r is not None:
+                if "end" in r:
+                    ret["end"] = r["end"]  # the latter end
+                if "start" in r and "end" in ret:  # there is an earlier start.
+                    # Remove end, merging this segment with the previous one.
+                    del ret["end"]
+        return ret if ret != {} else None
+
+
+if __name__ == "__main__":
+    # test/demonstrate the need for FixedVADIterator:
+
+    import torch
+
+    model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
+    vac = FixedVADIterator(model)
+    #   vac = VADIterator(model)  # the second case crashes with this
+
+    # this works: for both
+    audio_buffer = np.array([0] * (512), dtype=np.float32)
+    vac(audio_buffer)
+
+    # this crashes on the non FixedVADIterator with
+    # ops.prim.RaiseException("Input audio chunk is too short", "builtins.ValueError")
+    audio_buffer = np.array([0] * (512 - 1), dtype=np.float32)
+    vac(audio_buffer)

src/whisper_streaming/whisper_online.py

@@ -0,0 +1,400 @@
+#!/usr/bin/env python3
+import sys
+import numpy as np
+import librosa
+from functools import lru_cache
+import time
+import logging
+from src.whisper_streaming.backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR
+from src.whisper_streaming.online_asr import OnlineASRProcessor, VACOnlineASRProcessor
+
+logger = logging.getLogger(__name__)
+
+
+@lru_cache(10**6)
+def load_audio(fname):
+    a, _ = librosa.load(fname, sr=16000, dtype=np.float32)
+    return a
+
+
+def load_audio_chunk(fname, beg, end):
+    audio = load_audio(fname)
+    beg_s = int(beg * 16000)
+    end_s = int(end * 16000)
+    return audio[beg_s:end_s]
+
+WHISPER_LANG_CODES = "af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,he,hi,hr,ht,hu,hy,id,is,it,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh".split(
+    ","
+)
+
+
+def create_tokenizer(lan):
+    """returns an object that has split function that works like the one of MosesTokenizer"""
+
+    assert (
+        lan in WHISPER_LANG_CODES
+    ), "language must be Whisper's supported lang code: " + " ".join(WHISPER_LANG_CODES)
+
+    if lan == "uk":
+        import tokenize_uk
+
+        class UkrainianTokenizer:
+            def split(self, text):
+                return tokenize_uk.tokenize_sents(text)
+
+        return UkrainianTokenizer()
+
+    # supported by fast-mosestokenizer
+    if (
+        lan
+        in "as bn ca cs de el en es et fi fr ga gu hi hu is it kn lt lv ml mni mr nl or pa pl pt ro ru sk sl sv ta te yue zh".split()
+    ):
+        from mosestokenizer import MosesTokenizer
+
+        return MosesTokenizer(lan)
+
+    # the following languages are in Whisper, but not in wtpsplit:
+    if (
+        lan
+        in "as ba bo br bs fo haw hr ht jw lb ln lo mi nn oc sa sd sn so su sw tk tl tt".split()
+    ):
+        logger.debug(
+            f"{lan} code is not supported by wtpsplit. Going to use None lang_code option."
+        )
+        lan = None
+
+    from wtpsplit import WtP
+
+    # downloads the model from huggingface on the first use
+    wtp = WtP("wtp-canine-s-12l-no-adapters")
+
+    class WtPtok:
+        def split(self, sent):
+            return wtp.split(sent, lang_code=lan)
+
+    return WtPtok()
+
+
+def add_shared_args(parser):
+    """shared args for simulation (this entry point) and server
+    parser: argparse.ArgumentParser object
+    """
+    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-v3-turbo",
+        choices="tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo".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="auto",
+        help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
+    )
+    parser.add_argument(
+        "--task",
+        type=str,
+        default="transcribe",
+        choices=["transcribe", "translate"],
+        help="Transcribe or translate.",
+    )
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="faster-whisper",
+        choices=["faster-whisper", "whisper_timestamped", "mlx-whisper", "openai-api"],
+        help="Load only this backend for Whisper processing.",
+    )
+    parser.add_argument(
+        "--vac",
+        action="store_true",
+        default=False,
+        help="Use VAC = voice activity controller. Recommended. Requires torch.",
+    )
+    parser.add_argument(
+        "--vac-chunk-size", type=float, default=0.04, help="VAC sample size in seconds."
+    )
+    parser.add_argument(
+        "--vad",
+        action="store_true",
+        default=False,
+        help="Use VAD = voice activity detection, with the default parameters.",
+    )
+    parser.add_argument(
+        "--buffer_trimming",
+        type=str,
+        default="segment",
+        choices=["sentence", "segment"],
+        help='Buffer trimming strategy -- trim completed sentences marked with punctuation mark and detected by sentence segmenter, or the completed segments returned by Whisper. Sentence segmenter must be installed for "sentence" option.',
+    )
+    parser.add_argument(
+        "--buffer_trimming_sec",
+        type=float,
+        default=15,
+        help="Buffer trimming length threshold in seconds. If buffer length is longer, trimming sentence/segment is triggered.",
+    )
+    parser.add_argument(
+        "-l",
+        "--log-level",
+        dest="log_level",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Set the log level",
+        default="DEBUG",
+    )
+
+def backend_factory(args):
+    backend = args.backend
+    if backend == "openai-api":
+        logger.debug("Using OpenAI API.")
+        asr = OpenaiApiASR(lan=args.lan)
+    else:
+        if backend == "faster-whisper":
+            asr_cls = FasterWhisperASR
+        elif backend == "mlx-whisper":
+            asr_cls = MLXWhisper
+        else:
+            asr_cls = WhisperTimestampedASR
+
+        # Only for FasterWhisperASR and WhisperTimestampedASR
+        size = args.model
+        t = time.time()
+        logger.info(f"Loading Whisper {size} model for {args.lan}...")
+        asr = asr_cls(
+            modelsize=size,
+            lan=args.lan,
+            cache_dir=args.model_cache_dir,
+            model_dir=args.model_dir,
+        )
+        e = time.time()
+        logger.info(f"done. It took {round(e-t,2)} seconds.")
+
+    # Apply common configurations
+    if getattr(args, "vad", False):  # Checks if VAD argument is present and True
+        logger.info("Setting VAD filter")
+        asr.use_vad()
+
+    language = args.lan
+    if args.task == "translate":
+        asr.set_translate_task()
+        tgt_language = "en"  # Whisper translates into English
+    else:
+        tgt_language = language  # Whisper transcribes in this language
+
+    # Create the tokenizer
+    if args.buffer_trimming == "sentence":
+        tokenizer = create_tokenizer(tgt_language)
+    else:
+        tokenizer = None
+    return asr, tokenizer
+
+def online_factory(args, asr, tokenizer, logfile=sys.stderr):
+    if args.vac:
+        online = VACOnlineASRProcessor(
+            args.min_chunk_size,
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+        )
+    else:
+        online = OnlineASRProcessor(
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+        )
+    return online
+  
+def asr_factory(args, logfile=sys.stderr):
+    """
+    Creates and configures an ASR and ASR Online instance based on the specified backend and arguments.
+    """
+    asr, tokenizer = backend_factory(args)
+    online = online_factory(args, asr, tokenizer, logfile=logfile)
+    return asr, online
+
+def set_logging(args, logger, other="_server"):
+    logging.basicConfig(format="%(levelname)s\t%(message)s")  # format='%(name)s
+    logger.setLevel(args.log_level)
+    logging.getLogger("whisper_online" + other).setLevel(args.log_level)
+
+
+#    logging.getLogger("whisper_online_server").setLevel(args.log_level)
+
+
+if __name__ == "__main__":
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--audio_path",
+        type=str,
+        default='samples_jfk.wav',
+        help="Filename of 16kHz mono channel wav, on which live streaming is simulated.",
+    )
+    add_shared_args(parser)
+    parser.add_argument(
+        "--start_at",
+        type=float,
+        default=0.0,
+        help="Start processing audio at this time.",
+    )
+    parser.add_argument(
+        "--offline", action="store_true", default=False, help="Offline mode."
+    )
+    parser.add_argument(
+        "--comp_unaware",
+        action="store_true",
+        default=False,
+        help="Computationally unaware simulation.",
+    )
+
+    args = parser.parse_args()
+
+    # reset to store stderr to different file stream, e.g. open(os.devnull,"w")
+    logfile = sys.stderr
+
+    if args.offline and args.comp_unaware:
+        logger.error(
+            "No or one option from --offline and --comp_unaware are available, not both. Exiting."
+        )
+        sys.exit(1)
+
+    #    if args.log_level:
+    #        logging.basicConfig(format='whisper-%(levelname)s:%(name)s: %(message)s',
+    #                            level=getattr(logging, args.log_level))
+
+    set_logging(args, logger)
+
+    audio_path = args.audio_path
+
+    SAMPLING_RATE = 16000
+    duration = len(load_audio(audio_path)) / SAMPLING_RATE
+    logger.info("Audio duration is: %2.2f seconds" % duration)
+
+    asr, online = asr_factory(args, logfile=logfile)
+    if args.vac:
+        min_chunk = args.vac_chunk_size
+    else:
+        min_chunk = args.min_chunk_size
+
+    # load the audio into the LRU cache before we start the timer
+    a = load_audio_chunk(audio_path, 0, 1)
+
+    # warm up the ASR because the very first transcribe takes much more time than the other
+    asr.transcribe(a)
+
+    beg = args.start_at
+    start = time.time() - beg
+
+    def output_transcript(o, now=None):
+        # output format in stdout is like:
+        # 4186.3606 0 1720 Takhle to je
+        # - the first three words are:
+        #    - emission time from beginning of processing, in milliseconds
+        #    - 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
+        if now is None:
+            now = time.time() - start
+        if o[0] is not None:
+            print(
+                "%1.4f %1.0f %1.0f %s" % (now * 1000, o[0] * 1000, o[1] * 1000, o[2]),
+                file=logfile,
+                flush=True,
+            )
+            print(
+                "%1.4f %1.0f %1.0f %s" % (now * 1000, o[0] * 1000, o[1] * 1000, o[2]),
+                flush=True,
+            )
+        else:
+            # No text, so no output
+            pass
+
+    if args.offline:  ## offline mode processing (for testing/debugging)
+        a = load_audio(audio_path)
+        online.insert_audio_chunk(a)
+        try:
+            o = online.process_iter()
+        except AssertionError as e:
+            logger.error(f"assertion error: {repr(e)}")
+        else:
+            output_transcript(o)
+        now = None
+    elif args.comp_unaware:  # computational unaware mode
+        end = beg + min_chunk
+        while True:
+            a = load_audio_chunk(audio_path, beg, end)
+            online.insert_audio_chunk(a)
+            try:
+                o = online.process_iter()
+            except AssertionError as e:
+                logger.error(f"assertion error: {repr(e)}")
+                pass
+            else:
+                output_transcript(o, now=end)
+
+            logger.debug(f"## last processed {end:.2f}s")
+
+            if end >= duration:
+                break
+
+            beg = end
+
+            if end + min_chunk > duration:
+                end = duration
+            else:
+                end += min_chunk
+        now = duration
+
+    else:  # online = simultaneous mode
+        end = 0
+        while True:
+            now = time.time() - start
+            if now < end + min_chunk:
+                time.sleep(min_chunk + end - now)
+            end = time.time() - start
+            a = load_audio_chunk(audio_path, beg, end)
+            beg = end
+            online.insert_audio_chunk(a)
+
+            try:
+                o = online.process_iter()
+            except AssertionError as e:
+                logger.error(f"assertion error: {e}")
+                pass
+            else:
+                output_transcript(o)
+            now = time.time() - start
+            logger.debug(
+                f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}"
+            )
+
+            if end >= duration:
+                break
+        now = None
+
+    o = online.finish()
+    output_transcript(o, now=now)