add preliminary japanese support

whisperx/transcribe.py

@@ -1,21 +1,26 @@
 import argparse
 import os
 import warnings
-from typing import List, Optional, Tuple, Union, Iterator, TYPE_CHECKING
+from typing import TYPE_CHECKING, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 import torchaudio
 import tqdm
-from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram, load_audio
+from transformers import AutoProcessor, Wav2Vec2ForCTC
-from .alignment import get_trellis, backtrack, merge_repeats, merge_words
+
+from .alignment import backtrack, get_trellis, merge_repeats, merge_words
+from .audio import (HOP_LENGTH, N_FRAMES, SAMPLE_RATE, load_audio,
+                    log_mel_spectrogram, pad_or_trim)
 from .decoding import DecodingOptions, DecodingResult
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
-from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt, write_ass
+from .utils import (exact_div, format_timestamp, optional_float, optional_int,
+                    str2bool, write_ass, write_srt, write_txt, write_vtt)
 
 if TYPE_CHECKING:
     from .model import Whisper
 
+wa2vec2_on_hugginface = ["wav2vec2-large-xlsr-53-japanese"]
 
 def transcribe(
     model: "Whisper",
@@ -249,6 +254,7 @@ def transcribe(
 
 def align(
     transcript: Iterator[dict],
+    language: str,
     model: torch.nn.Module,
     model_dictionary: dict,
     audio: Union[str, np.ndarray, torch.Tensor],
@@ -278,12 +284,15 @@ def align(
 
         waveform_segment = audio[:, f1:f2]
         with torch.inference_mode():
-            emissions, _ = model(waveform_segment.to(device))
+            emissions = model(waveform_segment.to(device)).logits
             emissions = torch.log_softmax(emissions, dim=-1)
         emission = emissions[0].cpu().detach()
-
         transcription = segment['text'].strip()
-        t_words = transcription.split(' ')
+        if language != "ja":
+            t_words = transcription.split(' ')
+        else:
+            t_words = [c for c in transcription] #FIXME: ideally, we should use a tokenizer for Japanese to extract words
+
         t_words_clean = [''.join([w for w in word if w.lower() in model_dictionary.keys()]) for word in t_words]
         t_words_nonempty = [x for x in t_words_clean if x != ""]
         t_words_nonempty_idx = [x for x in range(len(t_words_clean)) if t_words_clean[x] != ""]
@@ -408,13 +417,19 @@ def cli():
         align_model = bundle.get_model().to(device)
         labels = bundle.get_labels()
         align_dictionary = {c.lower(): i for i, c in enumerate(labels)}
+    elif align_model == "wav2vec2-large-xlsr-53-japanese":
+        processor = AutoProcessor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
+        align_model = Wav2Vec2ForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
+        align_model.to(device)
+        labels = processor.tokenizer.get_vocab()
+        align_dictionary = processor.tokenizer.get_vocab()
     else:
         print(f'Align model "{align_model}" not found in torchaudio.pipelines, choose from:\n {torchaudio.pipelines.__all__}')
         raise ValueError(f'Align model "{align_model}" not found in torchaudio.pipelines')
     for audio_path in args.pop("audio"):
         result = transcribe(model, audio_path, temperature=temperature, **args)
-        result_aligned = align(result["segments"], align_model, align_dictionary, audio_path, device,
+        result_aligned = align(result["segments"], result["language"], align_model, align_dictionary, audio_path, device,
-                    extend_duration=align_extend, start_from_previous=align_from_prev)
+            extend_duration=align_extend, start_from_previous=align_from_prev)
         audio_basename = os.path.basename(audio_path)
 
         # save TXT