Generate subtitles (#315)

+#!/usr/bin/env python3
+#
+# Copyright (c)  2023  Xiaomi Corporation
+
+"""
+This file demonstrates how to use sherpa-onnx Python APIs to generate
+subtitles.
+
+Supported file formats are those supported by ffmpeg; for instance,
+*.mov, *.mp4, *.wav, etc.
+
+Note that you need a non-streaming model for this script.
+
+(1) For paraformer
+
+    ./python-api-examples/generate-subtitles.py  \
+      --silero-vad-model=/path/to/silero_vad.onnx \
+      --tokens=/path/to/tokens.txt \
+      --paraformer=/path/to/paraformer.onnx \
+      --num-threads=2 \
+      --decoding-method=greedy_search \
+      --debug=false \
+      --sample-rate=16000 \
+      --feature-dim=80 \
+      /path/to/test.mp4
+
+(2) For transducer models from icefall
+
+    ./python-api-examples/generate-subtitles.py  \
+      --silero-vad-model=/path/to/silero_vad.onnx \
+      --tokens=/path/to/tokens.txt \
+      --encoder=/path/to/encoder.onnx \
+      --decoder=/path/to/decoder.onnx \
+      --joiner=/path/to/joiner.onnx \
+      --num-threads=2 \
+      --decoding-method=greedy_search \
+      --debug=false \
+      --sample-rate=16000 \
+      --feature-dim=80 \
+      /path/to/test.mp4
+
+(3) For Whisper models
+
+./python-api-examples/generate-subtitles.py  \
+  --silero-vad-model=/path/to/silero_vad.onnx \
+  --whisper-encoder=./sherpa-onnx-whisper-base.en/base.en-encoder.int8.onnx \
+  --whisper-decoder=./sherpa-onnx-whisper-base.en/base.en-decoder.int8.onnx \
+  --tokens=./sherpa-onnx-whisper-base.en/base.en-tokens.txt \
+  --whisper-task=transcribe \
+  --num-threads=2 \
+  /path/to/test.mp4
+
+Please refer to
+https://k2-fsa.github.io/sherpa/onnx/index.html
+to install sherpa-onnx and to download non-streaming pre-trained models
+used in this file.
+"""
+import argparse
+import shutil
+import subprocess
+import sys
+from dataclasses import dataclass
+from datetime import timedelta
+from pathlib import Path
+
+import numpy as np
+import sherpa_onnx
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--silero-vad-model",
+        type=str,
+        required=True,
+        help="Path to silero_vad.onnx",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="Path to tokens.txt",
+    )
+
+    parser.add_argument(
+        "--encoder",
+        default="",
+        type=str,
+        help="Path to the transducer encoder model",
+    )
+
+    parser.add_argument(
+        "--decoder",
+        default="",
+        type=str,
+        help="Path to the transducer decoder model",
+    )
+
+    parser.add_argument(
+        "--joiner",
+        default="",
+        type=str,
+        help="Path to the transducer joiner model",
+    )
+
+    parser.add_argument(
+        "--paraformer",
+        default="",
+        type=str,
+        help="Path to the model.onnx from Paraformer",
+    )
+
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=1,
+        help="Number of threads for neural network computation",
+    )
+
+    parser.add_argument(
+        "--whisper-encoder",
+        default="",
+        type=str,
+        help="Path to whisper encoder model",
+    )
+
+    parser.add_argument(
+        "--whisper-decoder",
+        default="",
+        type=str,
+        help="Path to whisper decoder model",
+    )
+
+    parser.add_argument(
+        "--whisper-language",
+        default="",
+        type=str,
+        help="""It specifies the spoken language in the input file.
+        Example values: en, fr, de, zh, jp.
+        Available languages for multilingual models can be found at
+        https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
+        If not specified, we infer the language from the input audio file.
+        """,
+    )
+
+    parser.add_argument(
+        "--whisper-task",
+        default="transcribe",
+        choices=["transcribe", "translate"],
+        type=str,
+        help="""For multilingual models, if you specify translate, the output
+        will be in English.
+        """,
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Valid values are greedy_search and modified_beam_search.
+        modified_beam_search is valid only for transducer models.
+        """,
+    )
+    parser.add_argument(
+        "--debug",
+        type=bool,
+        default=False,
+        help="True to show debug messages when loading modes.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="""Sample rate of the feature extractor. Must match the one
+        expected by the model. Note: The input sound files can have a
+        different sample rate from this argument.""",
+    )
+
+    parser.add_argument(
+        "--feature-dim",
+        type=int,
+        default=80,
+        help="Feature dimension. Must match the one expected by the model",
+    )
+
+    parser.add_argument(
+        "sound_file",
+        type=str,
+        help="The input sound file to generate subtitles ",
+    )
+
+    return parser.parse_args()
+
+
+def assert_file_exists(filename: str):
+    assert Path(filename).is_file(), (
+        f"{filename} does not exist!\n"
+        "Please refer to "
+        "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    )
+
+
+def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
+    if args.encoder:
+        assert len(args.paraformer) == 0, args.paraformer
+        assert len(args.whisper_encoder) == 0, args.whisper_encoder
+        assert len(args.whisper_decoder) == 0, args.whisper_decoder
+
+        assert_file_exists(args.encoder)
+        assert_file_exists(args.decoder)
+        assert_file_exists(args.joiner)
+
+        recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
+            encoder=args.encoder,
+            decoder=args.decoder,
+            joiner=args.joiner,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            sample_rate=args.sample_rate,
+            feature_dim=args.feature_dim,
+            decoding_method=args.decoding_method,
+            debug=args.debug,
+        )
+    elif args.paraformer:
+        assert len(args.whisper_encoder) == 0, args.whisper_encoder
+        assert len(args.whisper_decoder) == 0, args.whisper_decoder
+
+        assert_file_exists(args.paraformer)
+
+        recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
+            paraformer=args.paraformer,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            sample_rate=args.sample_rate,
+            feature_dim=args.feature_dim,
+            decoding_method=args.decoding_method,
+            debug=args.debug,
+        )
+    elif args.whisper_encoder:
+        assert_file_exists(args.whisper_encoder)
+        assert_file_exists(args.whisper_decoder)
+
+        recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
+            encoder=args.whisper_encoder,
+            decoder=args.whisper_decoder,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            decoding_method=args.decoding_method,
+            debug=args.debug,
+            language=args.whisper_language,
+            task=args.whisper_task,
+        )
+    else:
+        raise ValueError("Please specify at least one model")
+
+    return recognizer
+
+
+@dataclass
+class Segment:
+    start: float
+    duration: float
+    text: str = ""
+
+    @property
+    def end(self):
+        return self.start + self.duration
+
+    def __str__(self):
+        s = f"{timedelta(seconds=self.start)}"[:-3]
+        s += " --> "
+        s += f"{timedelta(seconds=self.end)}"[:-3]
+        s = s.replace(".", ",")
+        s += "\n"
+        s += self.text
+        return s
+
+
+def main():
+    args = get_args()
+    assert_file_exists(args.tokens)
+    assert_file_exists(args.silero_vad_model)
+
+    assert args.num_threads > 0, args.num_threads
+
+    if not Path(args.sound_file).is_file():
+        raise ValueError(f"{args.sound_file} does not exist")
+
+    assert (
+        args.sample_rate == 16000
+    ), f"Only sample rate 16000 is supported.Given: {args.sample_rate}"
+
+    recognizer = create_recognizer(args)
+
+    ffmpeg_cmd = [
+        "ffmpeg",
+        "-i",
+        args.sound_file,
+        "-f",
+        "s16le",
+        "-acodec",
+        "pcm_s16le",
+        "-ac",
+        "1",
+        "-ar",
+        str(args.sample_rate),
+        "-",
+    ]
+
+    process = subprocess.Popen(
+        ffmpeg_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL
+    )
+
+    frames_per_read = int(args.sample_rate * 100)  # 100 second
+
+    stream = recognizer.create_stream()
+
+    config = sherpa_onnx.VadModelConfig()
+    config.silero_vad.model = args.silero_vad_model
+    config.silero_vad.min_silence_duration = 0.25
+    config.sample_rate = args.sample_rate
+
+    window_size = config.silero_vad.window_size
+
+    buffer = []
+    vad = sherpa_onnx.VoiceActivityDetector(config, buffer_size_in_seconds=100)
+
+    segment_list = []
+
+    print("Started!")
+
+    # TODO(fangjun): Support multithreads
+    while True:
+        # *2 because int16_t has two bytes
+        data = process.stdout.read(frames_per_read * 2)
+        if not data:
+            break
+
+        samples = np.frombuffer(data, dtype=np.int16)
+        samples = samples.astype(np.float32) / 32768
+
+        buffer = np.concatenate([buffer, samples])
+        while len(buffer) > window_size:
+            vad.accept_waveform(buffer[:window_size])
+            buffer = buffer[window_size:]
+
+        streams = []
+        segments = []
+        while not vad.empty():
+            segment = Segment(
+                start=vad.front.start / args.sample_rate,
+                duration=len(vad.front.samples) / args.sample_rate,
+            )
+            segments.append(segment)
+
+            stream = recognizer.create_stream()
+            stream.accept_waveform(args.sample_rate, vad.front.samples)
+
+            streams.append(stream)
+
+            vad.pop()
+
+        recognizer.decode_streams(streams)
+        for seg, stream in zip(segments, streams):
+            seg.text = stream.result.text
+            segment_list.append(seg)
+
+    srt_filename = Path(args.sound_file).with_suffix(".srt")
+    with open(srt_filename, "w", encoding="utf-8") as f:
+        for i, seg in enumerate(segment_list):
+            print(i + 1, file=f)
+            print(seg, file=f)
+            print("", file=f)
+
+    print(f"Saved to {srt_filename}")
+    print("Done!")
+
+
+if __name__ == "__main__":
+    if shutil.which("ffmpeg") is None:
+        sys.exit("Please install ffmpeg first!")
+    main()