Transcript bound to audio video

backend/src/app/core/data/crud/document_tag.py

@@ -1,4 +1,4 @@
-from typing import Dict, List
+from typing import Dict, List, Optional
 
 from sqlalchemy import delete, func, select
 from sqlalchemy.orm import Session

backend/src/app/core/data/crud/project_metadata.py

@@ -5,6 +5,7 @@
 
 from app.core.data.crud.crud_base import CRUDBase
 from app.core.data.crud.source_document_metadata import crud_sdoc_meta
+from app.core.data.doc_type import DocType
 from app.core.data.dto.project_metadata import (
     ProjectMetadataCreate,
     ProjectMetadataUpdate,

backend/src/app/preprocessing/pipeline/__init__.py

@@ -8,6 +8,9 @@
 def build_text_pipeline(
     is_init: bool = True,
 ) -> PreprocessingPipeline:
+    from app.preprocessing.pipeline.steps.common.detect_content_language import (
+        detect_content_language,
+    )
     from app.preprocessing.pipeline.steps.common.remove_erroneous_sdoc import (
         remove_erroneous_or_unfinished_sdocs,
     )
@@ -27,9 +30,6 @@ def build_text_pipeline(
     from app.preprocessing.pipeline.steps.text.create_pptd import (
         create_pptd,
     )
-    from app.preprocessing.pipeline.steps.text.detect_content_language import (
-        detect_content_language,
-    )
     from app.preprocessing.pipeline.steps.text.extract_content_in_html_from_raw_text_docs import (
         extract_content_in_html_from_raw_text_docs,
     )
@@ -210,7 +210,7 @@ def build_image_pipeline(
         run_object_detection,
     )
     from app.preprocessing.pipeline.steps.image.store_metadata_to_database import (
-        store_metadata_to_database,
+        store_metadata_and_data_to_database,
     )
     from app.preprocessing.pipeline.steps.image.write_ppid_to_database import (
         write_ppid_to_database,
@@ -277,7 +277,7 @@ def build_image_pipeline(
 
     pipeline.register_step(
         func=create_pptd_from_caption,
-        required_data=["ppid", "sdoc_id"],
+        required_data=["ppid"],
     )
 
     # run caption through spacy and add to elasticsearch to make it searchable
@@ -307,7 +307,7 @@ def build_image_pipeline(
     )
 
     pipeline.register_step(
-        func=store_metadata_to_database,
+        func=store_metadata_and_data_to_database,
         required_data=[
             "pptd",
             "ppid",
@@ -334,9 +334,6 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     # we need to import the steps here to avoid loading models at startup
     # in the api worker!
     from app.preprocessing.pipeline.steps.audio.convert_to_pcm import convert_to_pcm
-    from app.preprocessing.pipeline.steps.audio.create_and_store_transcript_file import (
-        create_and_store_transcript_file,
-    )
     from app.preprocessing.pipeline.steps.audio.create_ffmpeg_probe_audio_metadata import (
         create_ffmpeg_probe_audio_metadata,
     )
@@ -350,9 +347,15 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     from app.preprocessing.pipeline.steps.audio.generate_webp_thumbnail_for_audio import (
         generate_webp_thumbnail_for_audio,
     )
+    from app.preprocessing.pipeline.steps.audio.store_metadata_to_database import (
+        store_metadata_and_data_to_database,
+    )
     from app.preprocessing.pipeline.steps.audio.write_ppad_to_database import (
         write_ppad_to_database,
     )
+    from app.preprocessing.pipeline.steps.common.detect_content_language import (
+        detect_content_language,
+    )
     from app.preprocessing.pipeline.steps.common.remove_erroneous_sdoc import (
         remove_erroneous_or_unfinished_sdocs,
     )
@@ -362,8 +365,22 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     from app.preprocessing.pipeline.steps.common.update_sdoc_status_to_finish import (
         update_sdoc_status_to_finish,
     )
+    from app.preprocessing.pipeline.steps.text.generate_keywords import (
+        generate_keywords,
+    )
+    from app.preprocessing.pipeline.steps.text.generate_sentence_annotations import (
+        generate_sentence_annotations,
+    )
+    from app.preprocessing.pipeline.steps.text.generate_word_frequencies import (
+        generate_word_frequncies,
+    )
+    from app.preprocessing.pipeline.steps.text.run_spacy_pipeline import (
+        run_spacy_pipeline,
+    )
+    from app.preprocessing.pipeline.steps.text.store_document_in_elasticsearch import (
+        store_document_in_elasticsearch,
+    )
 
-    text_pipeline = build_text_pipeline()
     pipeline = PreprocessingPipeline(doc_type=DocType.audio)
 
     pipeline.register_step(
@@ -392,17 +409,45 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     )
 
     pipeline.register_step(
-        func=create_and_store_transcript_file,
+        func=write_ppad_to_database,
         required_data=["ppad"],
     )
 
+    # instead create pptd before and now add it as metadata
     pipeline.register_step(
         func=create_pptd_from_transcription,
         required_data=["ppad"],
     )
-    pipeline.join_pipeline(
-        pipeline=text_pipeline,
-        skip_steps_with_name=["create_pptd"],
+
+    pipeline.register_step(
+        func=detect_content_language,
+        required_data=["pptd"],
+    )
+
+    # run caption through spacy and add to elasticsearch to make it searchable
+    pipeline.register_step(
+        func=run_spacy_pipeline,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=generate_word_frequncies,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=generate_keywords,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=generate_sentence_annotations,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=store_document_in_elasticsearch,
+        required_data=["pptd", "sdoc_id"],
     )
 
     pipeline.register_step(
@@ -413,8 +458,12 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     )
 
     pipeline.register_step(
-        func=write_ppad_to_database,
-        required_data=["ppad"],
+        func=store_metadata_and_data_to_database,
+        required_data=[
+            "pptd",
+            "ppad",
+            "sdoc_id",
+        ],
     )
 
     pipeline.register_step(
@@ -433,6 +482,19 @@ def build_audio_pipeline(foo: str = "bar") -> PreprocessingPipeline:
 
 @lru_cache(maxsize=1)
 def build_video_pipeline(foo: str = "bar") -> PreprocessingPipeline:
+    from app.preprocessing.pipeline.steps.audio.convert_to_pcm import convert_to_pcm
+    from app.preprocessing.pipeline.steps.audio.create_ffmpeg_probe_audio_metadata import (
+        create_ffmpeg_probe_audio_metadata,
+    )
+    from app.preprocessing.pipeline.steps.audio.create_pptd_from_transcription import (
+        create_pptd_from_transcription,
+    )
+    from app.preprocessing.pipeline.steps.audio.generate_automatic_transcription import (
+        generate_automatic_transcription,
+    )
+    from app.preprocessing.pipeline.steps.common.detect_content_language import (
+        detect_content_language,
+    )
     from app.preprocessing.pipeline.steps.common.remove_erroneous_sdoc import (
         remove_erroneous_or_unfinished_sdocs,
     )
@@ -442,8 +504,20 @@ def build_video_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     from app.preprocessing.pipeline.steps.common.update_sdoc_status_to_finish import (
         update_sdoc_status_to_finish,
     )
-    from app.preprocessing.pipeline.steps.video.add_word_level_transcriptions_to_ppvd_metadata import (
-        add_word_level_transcriptions_to_ppvd_metadata,
+    from app.preprocessing.pipeline.steps.text.generate_keywords import (
+        generate_keywords,
+    )
+    from app.preprocessing.pipeline.steps.text.generate_sentence_annotations import (
+        generate_sentence_annotations,
+    )
+    from app.preprocessing.pipeline.steps.text.generate_word_frequencies import (
+        generate_word_frequncies,
+    )
+    from app.preprocessing.pipeline.steps.text.run_spacy_pipeline import (
+        run_spacy_pipeline,
+    )
+    from app.preprocessing.pipeline.steps.text.store_document_in_elasticsearch import (
+        store_document_in_elasticsearch,
     )
     from app.preprocessing.pipeline.steps.video.create_and_store_audio_stream_file import (
         create_and_store_audio_stream_file,
@@ -458,11 +532,13 @@ def build_video_pipeline(foo: str = "bar") -> PreprocessingPipeline:
     from app.preprocessing.pipeline.steps.video.generate_webp_thumbnail_for_video import (
         generate_webp_thumbnail_for_video,
     )
+    from app.preprocessing.pipeline.steps.video.store_metadata_to_database import (
+        store_metadata_and_data_to_database,
+    )
     from app.preprocessing.pipeline.steps.video.write_ppvd_to_database import (
         write_ppvd_to_database,
     )
 
-    audio_pipeline = build_audio_pipeline()
     pipeline = PreprocessingPipeline(doc_type=DocType.video)
 
     pipeline.register_step(
@@ -490,14 +566,71 @@ def build_video_pipeline(foo: str = "bar") -> PreprocessingPipeline:
         required_data=["ppvd"],
     )
 
-    pipeline.join_pipeline(
-        pipeline=audio_pipeline,
-        skip_steps_with_name=["create_ppad"],
+    pipeline.register_step(
+        func=write_ppvd_to_database,
+        required_data=["ppvd"],
+    )
+
+    pipeline.register_step(
+        func=create_ffmpeg_probe_audio_metadata,
+        required_data=["ppad"],
+    )
+
+    pipeline.register_step(
+        func=convert_to_pcm,
+        required_data=["ppad"],
+    )
+
+    pipeline.register_step(
+        func=generate_automatic_transcription,
+        required_data=["ppad"],
+    )
+
+    # instead create pptd before and now add it as metadata
+    pipeline.register_step(
+        func=create_pptd_from_transcription,
+        required_data=["ppad"],
+    )
+
+    pipeline.register_step(
+        func=detect_content_language,
+        required_data=["pptd"],
+    )
+
+    # run caption through spacy and add to elasticsearch to make it searchable
+    pipeline.register_step(
+        func=run_spacy_pipeline,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=generate_word_frequncies,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=generate_keywords,
+        required_data=["pptd"],
     )
 
     pipeline.register_step(
-        func=add_word_level_transcriptions_to_ppvd_metadata,
-        required_data=["ppvd", "ppad"],
+        func=generate_sentence_annotations,
+        required_data=["pptd"],
+    )
+
+    pipeline.register_step(
+        func=store_document_in_elasticsearch,
+        required_data=["pptd", "sdoc_id"],
+    )
+
+    pipeline.register_step(
+        func=store_metadata_and_data_to_database,
+        required_data=[
+            "pptd",
+            "ppad",
+            "ppvd",
+            "sdoc_id",
+        ],
     )
 
     pipeline.register_step(
@@ -507,11 +640,6 @@ def build_video_pipeline(foo: str = "bar") -> PreprocessingPipeline:
         func=remove_erroneous_or_unfinished_sdocs,
     )
 
-    pipeline.register_step(
-        func=write_ppvd_to_database,
-        required_data=["ppvd"],
-    )
-
     pipeline.register_step(
         func=resolve_sdoc_links,
     )

backend/src/app/preprocessing/pipeline/steps/audio/create_pptd_from_transcription.py

@@ -1,21 +1,22 @@
+from pathlib import Path
+
 from app.preprocessing.pipeline.model.audio.preproaudiodoc import PreProAudioDoc
 from app.preprocessing.pipeline.model.pipeline_cargo import PipelineCargo
 from app.preprocessing.pipeline.model.text.preprotextdoc import PreProTextDoc
 
 
 def create_pptd_from_transcription(cargo: PipelineCargo) -> PipelineCargo:
     ppad: PreProAudioDoc = cargo.data["ppad"]
-
-    if not ppad.transcript_filepath.exists():
-        raise FileNotFoundError(
-            f"The transcription file {ppad.transcript_filepath} "
-            f"for {cargo.ppj_payload.filename} does not exist!"
-        )
-
+    ppad.metadata["transcription"] = " ".join(
+        [a.text for a in ppad.word_level_transcriptions]
+    )
     pptd = PreProTextDoc(
-        filename=ppad.transcript_filepath.name,
-        filepath=ppad.transcript_filepath,
-        project_id=cargo.ppj_payload.project_id,
+        filepath=Path("/this/is/a/fake_path.txt"),
+        filename="fake_path.txt",
+        project_id=ppad.project_id,
+        text=ppad.metadata["transcription"],
+        html=f"<html><body><p>{ppad.metadata['transcription']}</p></body></html>",
+        metadata={"language": "en"},
         mime_type="text/plain",
     )