Add subtitles translation using EasyNMT and OpusMT libraries

.github/workflows/pylint.yml

@@ -18,6 +18,8 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pylint
+        pip install pybind11
+        pip install fasttext
         pip install -r requirements.txt
     - name: Analysing the code with pylint
       run: |

.github/workflows/setup.yml

@@ -0,0 +1,24 @@
+name: Setup
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9"]
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install application
+      run: |
+        pip install wheel
+        pip install fasttext
+        pip install -e .
+    - name: Check that package was installed successfully
+      run: |
+        faster_auto_subtitle -h

README.md

@@ -37,6 +37,12 @@ Adding `--task translate` will translate the subtitles into English:
 
     faster_auto_subtitle /path/to/video.mp4 --task translate
 
+Adding `--target_language {2-letter-language-code}` will translate the subtitles into specified language using [Opus-MT](https://github.com/Helsinki-NLP/Opus-MT):
+
+    faster_auto_subtitle /path/to/video.mp4 --target_language fr
+
+This will require downloading the appropriate model. If direct translation is not available it will attempt translation from source to english and from english to source.
+
 Run the following to view all available options:
 
     faster_auto_subtitle --help
@@ -49,7 +55,7 @@ Higher `beam_size` usually leads to greater accuracy, but slows down the process
 
 Setting higher `no_speech_threshold` could be useful for videos with a lot of background noise to stop Whisper from "hallucinating" subtitles for it.
 
-In my experience settings option `condition_on_previous_text` to `False` dramatically increases accurracy for videos like TV Shows with an intro song at the start. 
+In my experience settings option `condition_on_previous_text` to `False` dramatically increases accurracy for videos like TV Shows with an intro song at the start.
 
 You can use `sample_interval` parameter to generate subtitles for a portion of the video to play around with those parameters:
 

auto_subtitle/cli.py

@@ -46,11 +46,16 @@ def main():
     parser.add_argument("--task", type=str, default="transcribe",
                         choices=["transcribe", "translate"],
                         help="whether to perform X->X speech recognition ('transcribe') \
-                              or X->English translation ('translate')")
+                              or X->Language translation ('translate')")
     parser.add_argument("--language", type=str, default="auto",
                         choices=LANGUAGE_CODES,
                         help="What is the origin language of the video? \
                               If unset, it is detected automatically.")
+    parser.add_argument("--target_language", type=str, default="en",
+                        choices=LANGUAGE_CODES,
+                        help="Desired language to translate subtitles to. \
+                              If language is not en, Opus-MT will be used. \
+                              See https://github.com/Helsinki-NLP/Opus-MT.")
 
     args = parser.parse_args().__dict__
 

auto_subtitle/main.py

@@ -1,9 +1,9 @@
 import os
 import warnings
-import tempfile
 from .utils.files import filename, write_srt
 from .utils.ffmpeg import get_audio, overlay_subtitles
 from .utils.whisper import WhisperAI
+from .translation.easynmt_utils import EasyNMTWrapper
 
 
 def process(args: dict):
@@ -12,52 +12,94 @@ def process(args: dict):
     output_srt: bool = args.pop("output_srt")
     srt_only: bool = args.pop("srt_only")
     language: str = args.pop("language")
-    sample_interval: str = args.pop("sample_interval")
+    sample_interval: list = args.pop("sample_interval")
+    target_language: str = args.pop("target_language")
 
     os.makedirs(output_dir, exist_ok=True)
 
     if model_name.endswith(".en"):
         warnings.warn(
             f"{model_name} is an English-only model, forcing English detection.")
         args["language"] = "en"
+        language = "en"
     # if translate task used and language argument is set, then use it
     elif language != "auto":
         args["language"] = language
 
+    if target_language != 'en':
+        warnings.warn(
+            f"{target_language} is not English, Opus-MT will be used to perform translation.")
+        args['task'] = 'transcribe'
+
     audios = get_audio(args.pop("video"), args.pop(
         'audio_channel'), sample_interval)
 
-    model_args = {}
-    model_args["model_size_or_path"] = model_name
-    model_args["device"] = args.pop("device")
-    model_args["compute_type"] = args.pop("compute_type")
+    model_args = {
+        "model_size_or_path": model_name,
+        "device": args.pop("device"),
+        "compute_type": args.pop("compute_type")
+    }
+
+    subtitles = get_subtitles(audios, model_args, args)
+    print('Subtitles generated.')
+
+    if target_language != 'en':
+        print('Translating subtitles... This might take a while.')
+        subtitles = translate_subtitles(
+            subtitles, language, target_language, model_args)
 
-    srt_output_dir = output_dir if output_srt or srt_only else tempfile.gettempdir()
-    subtitles = get_subtitles(audios, srt_output_dir, model_args, args)
+    if output_srt or srt_only:
+        print('Saving subtitle files...')
+        save_subtitles(subtitles, output_dir)
 
     if srt_only:
         return
 
     overlay_subtitles(subtitles, output_dir, sample_interval)
 
 
-def get_subtitles(audio_paths: list, output_dir: str,
-                  model_args: dict, transcribe_args: dict):
+def translate_subtitles(subtitles: dict, source_lang: str, target_lang: str, model_args: dict):
+    model = EasyNMTWrapper(device=model_args['device'])
+
+    translated_subtitles = {}
+    for key, subtitle in subtitles.items():
+        src_lang = source_lang
+        if src_lang == '' or src_lang is None:
+            src_lang = subtitle['language']
+
+        translated_segments = model.translate(
+            subtitle['segments'], src_lang, target_lang)
+
+        translated_subtitle = subtitle.copy()
+        translated_subtitle['segments'] = translated_segments
+        translated_subtitles[key] = translated_subtitle
+
+    return translated_subtitles
+
+
+def save_subtitles(subtitles: dict, output_dir: str):
+    for path, subtitle in subtitles.items():
+        subtitle["output_path"] = os.path.join(
+            output_dir, f"{filename(path)}.srt")
+
+        print(f'Saving to path {subtitle["output_path"]}')
+        with open(subtitle['output_path'], "w", encoding="utf-8") as srt:
+            write_srt(subtitle['segments'], file=srt)
+
+
+def get_subtitles(audio_paths: dict, model_args: dict, transcribe_args: dict):
     model = WhisperAI(model_args, transcribe_args)
 
-    subtitles_path = {}
+    subtitles = {}
 
     for path, audio_path in audio_paths.items():
         print(
             f"Generating subtitles for {filename(path)}... This might take a while."
         )
-        srt_path = os.path.join(output_dir, f"{filename(path)}.srt")
-
-        segments = model.transcribe(audio_path)
 
-        with open(srt_path, "w", encoding="utf-8") as srt:
-            write_srt(segments, file=srt)
+        segments, info = model.transcribe(audio_path)
 
-        subtitles_path[path] = srt_path
+        subtitles[path] = {'segments': list(
+            segments), 'language': info.language}
 
-    return subtitles_path
+    return subtitles

auto_subtitle/translation/easynmt_utils.py

@@ -0,0 +1,24 @@
+from easynmt import EasyNMT
+from faster_whisper.transcribe import Segment
+from .opusmt_utils import OpusMT
+
+
+class EasyNMTWrapper:
+    def __init__(self, device):
+        self.translator = OpusMT()
+        self.model = EasyNMT('opus-mt',
+                             translator=self.translator,
+                             device=device if device != 'auto' else None)
+
+    def translate(self, segments: list[Segment], source_lang: str, target_lang: str):
+        source_text = [segment.text for segment in segments]
+        self.translator.load_available_models()
+
+        translated_text = self.model.translate(source_text, target_lang,
+                                               source_lang, show_progress_bar=True)
+        translated_segments = [None] * len(segments)
+        for index, segment in enumerate(segments):
+            translated_segments[index] = segment._replace(
+                text=translated_text[index])
+
+        return translated_segments

auto_subtitle/translation/languages.py

@@ -0,0 +1,20 @@
+import langcodes
+from transformers.models.marian.convert_marian_tatoeba_to_pytorch import GROUP_MEMBERS
+
+
+def to_alpha2_languages(languages):
+    return set(item for sublist in [__to_alpha2_language(language) for language in languages] for item in sublist)
+
+
+def __to_alpha2_language(language):
+    if len(language) == 2:
+        return [language]
+
+    if language in GROUP_MEMBERS:
+        return set([langcodes.Language.get(x).language for x in GROUP_MEMBERS[language][1]])
+
+    return [langcodes.Language.get(language).language]
+
+
+def to_alpha3_language(language):
+    return langcodes.Language.get(language).to_alpha3()

auto_subtitle/translation/opusmt_utils.py

@@ -0,0 +1,149 @@
+import time
+import logging
+from typing import List
+import torch
+from huggingface_hub import list_models, ModelFilter
+from transformers import MarianMTModel, MarianTokenizer
+from .languages import to_alpha2_languages, to_alpha3_language
+
+logger = logging.getLogger(__name__)
+
+NLP_ROOT = 'Helsinki-NLP'
+
+
+class OpusMT:
+    def __init__(self, max_loaded_models: int = 10):
+        self.models = {}
+        self.max_loaded_models = max_loaded_models
+        self.max_length = None
+
+        self.available_models = None
+        self.translations_graph = None
+
+    def load_model(self, model_name):
+        if model_name in self.models:
+            self.models[model_name]['last_loaded'] = time.time()
+            return self.models[model_name]['tokenizer'], self.models[model_name]['model']
+
+        logger.info("Load model: {}" % model_name)
+        tokenizer = MarianTokenizer.from_pretrained(model_name)
+        model = MarianMTModel.from_pretrained(model_name)
+        model.eval()
+
+        if len(self.models) >= self.max_loaded_models:
+            oldest_time = time.time()
+            oldest_model = None
+            for loaded_model_name in self.models.keys():
+                if self.models[loaded_model_name]['last_loaded'] <= oldest_time:
+                    oldest_model = loaded_model_name
+                    oldest_time = self.models[loaded_model_name]['last_loaded']
+            del self.models[oldest_model]
+
+        self.models[model_name] = {
+            'tokenizer': tokenizer, 'model': model, 'last_loaded': time.time()}
+        return tokenizer, model
+
+    def load_available_models(self):
+        if self.available_models is not None:
+            return
+
+        print('Loading a list of available language models from OPUS-NT')
+        model_list = list_models(
+            filter=ModelFilter(
+                author=NLP_ROOT
+            )
+        )
+
+        suffix = [x.modelId.split("/")[1] for x in model_list
+                  if x.modelId.startswith(f'{NLP_ROOT}/opus-mt') and 'tc' not in x.modelId]
+
+        models = [DownloadableModel(f"{NLP_ROOT}/{s}")
+                  for s in suffix if s == s.lower()]
+
+        self.available_models = {}
+        for model in models:
+            for src in model.source_languages:
+                for tgt in model.target_languages:
+                    key = f'{src}-{tgt}'
+                    if key not in self.available_models:
+                        self.available_models[key] = model
+                    elif self.available_models[key].language_count > model.language_count:
+                        self.available_models[key] = model
+
+    def determine_required_translations(self, source_lang, target_lang):
+        direct_key = f'{source_lang}-{target_lang}'
+        if direct_key in self.available_models:
+            print(
+                f'Found direct translation from {source_lang} to {target_lang}.')
+            return [(source_lang, target_lang, direct_key)]
+
+        print(
+            f'No direct translation from {source_lang} to {target_lang}. Trying to translate through en.')
+
+        to_en_key = f'{source_lang}-en'
+        if to_en_key not in self.available_models:
+            print(f'No translation from {source_lang} to en.')
+            return []
+
+        from_en_key = f'en-{target_lang}'
+        if from_en_key not in self.available_models:
+            print(f'No translation from en to {target_lang}.')
+            return []
+
+        return [(source_lang, 'en', to_en_key), ('en', target_lang, from_en_key)]
+
+    def translate_sentences(self, sentences: List[str], source_lang: str, target_lang: str, device: str, beam_size: int = 5, **kwargs):
+        self.load_available_models()
+
+        translations = self.determine_required_translations(
+            source_lang, target_lang)
+
+        if len(translations) == 0:
+            return sentences
+
+        intermediate = sentences
+        for _, tgt_lang, key in translations:
+            model_data = self.available_models[key]
+            model_name = model_data.name
+            tokenizer, model = self.load_model(model_name)
+            model.to(device)
+
+            if model_data.multilanguage:
+                alpha3 = to_alpha3_language(tgt_lang)
+                prefix = next(
+                    x for x in tokenizer.supported_language_codes if alpha3 in x)
+                intermediate = [f'{prefix} {x}' for x in intermediate]
+
+            inputs = tokenizer(intermediate, truncation=True, padding=True,
+                               max_length=self.max_length, return_tensors="pt")
+
+            for key in inputs:
+                inputs[key] = inputs[key].to(device)
+
+            with torch.no_grad():
+                translated = model.generate(
+                    **inputs, num_beams=beam_size, **kwargs)
+                intermediate = [tokenizer.decode(
+                    t, skip_special_tokens=True) for t in translated]
+
+        return intermediate
+
+
+class DownloadableModel:
+    def __init__(self, name):
+        self.name = name
+        source_languages, target_languages = self.parse_languages(name)
+        self.source_languages = source_languages
+        self.target_languages = target_languages
+        self.multilanguage = len(self.target_languages) > 1
+        self.language_count = len(
+            self.source_languages) + len(self.target_languages)
+
+    @staticmethod
+    def parse_languages(name):
+        parts = name.split('-')
+        if len(parts) > 5:
+            return set(), set()
+
+        src, tgt = parts[3], parts[4]
+        return to_alpha2_languages(src.split('_')), to_alpha2_languages(tgt.split('_'))