🔄 Update ScriptedVideoPipeline to enhance performance and structure

-  Enhanced script generation by introducing iterative chapter processing.
- 🔊 Refactored text-to-speech integration to handle individual chapters separately, improving audio synchronization.
- 🎞️ Modified video and asset generation workflow to align with dynamic content from each chapter.
- 🐛 Fixed audio transcription segmentation to accurately split sentences, ensuring better subtitle accuracy.
- 🧹 Cleaned up and structured the code for better readability and maintenance.
This commit is contained in:
2024-05-16 14:02:50 +02:00
parent dbede558c4
commit d817420309

View File

@@ -45,7 +45,11 @@ class ScriptedVideoPipeline(BasePipeline):
)["chapters"]
ctx.script = ""
for chapter in chapters:
text_audio = []
ctx.duration = 0
for i, chapter in enumerate(chapters):
ctx.progress(0.2, f"Generating chapter: {chapter['title']}...")
system = prompts["writer"]["system"]
chat = prompts["writer"]["chat"]
@@ -54,87 +58,96 @@ class ScriptedVideoPipeline(BasePipeline):
.replace("{chapter_title}", chapter["title"])
.replace("{chapter_instructions}", chapter["explanation"])
)
ctx.script += ctx.powerfulllmengine.generate(
script = ctx.powerfulllmengine.generate(
system_prompt=system,
chat_prompt=chat,
temperature=1,
max_tokens=4096,
json_mode=True,
)["chapter"]
ctx.script += script
ctx.script += "\n"
ctx.progress(0.3, "Synthesizing voice...")
ctx.duration = ctx.ttsengine.synthesize(
ctx.script, ctx.get_file_path("tts.wav")
)
ctx.audio.append(mp.AudioFileClip(ctx.get_file_path("tts.wav")))
ctx.progress(0.4, "Transcribing audio...")
ctx.timed_script = ctx.transcriptionengine.transcribe(
ctx.get_file_path("tts.wav"), fast=False, words=True
)
ctx.progress(0.3, "Synthesizing voice...")
duration = ctx.ttsengine.synthesize(
script, ctx.get_file_path(f"tts_{i}.wav")
)
audioclip = mp.AudioFileClip(ctx.get_file_path(f"tts_{i}.wav"))
audioclip = audioclip.with_start(ctx.duration)
text_audio.append(audioclip)
ctx.progress(0.2, f"Transcribing chapter: {chapter['title']}...")
timed_script = ctx.transcriptionengine.transcribe(
ctx.get_file_path(f"tts_{i}.wav"), fast=False, words=True
)
sentence_split_script = []
current_sentence = None
sentence_split_script = []
current_sentence = None
for word in ctx.timed_script.copy():
if current_sentence is None:
# Initialize the first sentence
current_sentence = {
"text": word["text"],
"end": word["end"],
"start": word["start"],
}
elif word["text"].endswith((".", "!", "?")):
# Add the word to the current sentence and finalize it
current_sentence["text"] += f" {word['text']}"
current_sentence["end"] = word["end"]
for word in timed_script.copy():
if current_sentence is None:
# Initialize the first sentence
current_sentence = {
"text": word["text"],
"end": word["end"],
"start": word["start"],
}
elif word["text"].endswith((".", "!", "?")):
# Add the word to the current sentence and finalize it
current_sentence["text"] += f" {word['text']}"
current_sentence["end"] = word["end"]
sentence_split_script.append(current_sentence)
current_sentence = None # Prepare to start a new sentence
else:
# Continue adding words to the current sentence
current_sentence["text"] += f" {word['text']}"
current_sentence["end"] = word["end"]
# If the last sentence didn't end with a punctuation mark
if current_sentence is not None:
sentence_split_script.append(current_sentence)
current_sentence = None # Prepare to start a new sentence
else:
# Continue adding words to the current sentence
current_sentence["text"] += f" {word['text']}"
current_sentence["end"] = word["end"]
# If the last sentence didn't end with a punctuation mark
if current_sentence is not None:
sentence_split_script.append(current_sentence)
ctx.progress(0.5, "Generating images...")
system = prompts["imager"]["system"]
chat = prompts["imager"]["chat"]
chat = chat.replace("{user_instructions}", str(self.user_instructions))
chat = chat.replace("{assets_instructions}", str(self.assets_instructions))
chat = chat.replace("{video_transcript}", str(sentence_split_script))
assets: list[dict[str, str | float]] = ctx.powerfulllmengine.generate(
system_prompt=system,
chat_prompt=chat,
temperature=1,
max_tokens=4096,
json_mode=True,
)["assets"]
for i, asset in enumerate(assets):
if asset["type"] == "stock":
ctx.progress(0.5, f"Getting stock image {i + 1}...")
ctx.index_4.append(
ctx.stockimageengine.get(
asset["query"], asset["start"], asset["end"]
ctx.progress(0.2, f"Generating video for chapter: {chapter['title']}...")
system = prompts["imager"]["system"]
chat = prompts["imager"]["chat"]
chat = chat.replace("{user_instructions}", str(self.user_instructions))
chat = chat.replace("{assets_instructions}", str(self.assets_instructions))
chat = chat.replace("{video_transcript}", str(sentence_split_script))
assets: list[dict[str, str | float]] = ctx.powerfulllmengine.generate(
system_prompt=system,
chat_prompt=chat,
temperature=1,
max_tokens=4096,
json_mode=True,
)["assets"]
for i, asset in enumerate(assets):
if asset["type"] == "stock":
ctx.progress(0.5, f"Getting stock image {i + 1}...")
ctx.index_4.append(
ctx.stockimageengine.get(
asset["query"],
asset["start"] + ctx.duration,
asset["end"] + ctx.duration,
)
)
)
elif asset["type"] == "ai":
ctx.progress(0.5, f"Generating AI image {i + 1}...")
ctx.index_5.append(
ctx.aiimageengine.generate(
asset["prompt"], asset["start"], asset["end"]
elif asset["type"] == "ai":
ctx.progress(0.5, f"Generating AI image {i + 1}...")
ctx.index_5.append(
ctx.aiimageengine.generate(
asset["prompt"],
asset["start"] + ctx.duration,
asset["end"] + ctx.duration,
)
)
)
ctx.duration += duration + 0.5
ctx.audio.extend(text_audio)
if not isinstance(ctx.audiobackgroundengine, engines.NoneEngine):
ctx.progress(0.6, "Generating audio background...")
ctx.audio.append(ctx.audiobackgroundengine.get_background())
if not isinstance(ctx.backgroundengine, engines.NoneEngine):
ctx.progress(0.65, "Generating background...")
ctx.audio.append(ctx.backgroundengine.get_background())
ctx.index_0.append(ctx.backgroundengine.get_background())
ctx.progress(0.7, "Rendering video...")
clips = [
@@ -230,11 +243,13 @@ class ScriptedVideoPipeline(BasePipeline):
lines=4,
max_lines=6,
label="Video instructions",
info="Explain what the video should be about, how many chapters, and any specific instructions.",
),
gr.Textbox(
lines=4,
max_lines=6,
label="Assets only instructions",
info="Explain how the assets should be used in the video. When, how many, and of what type (stock images, AI or both)",
),
ratio,
width,