Delete app.py

app.py

@@ -1,329 +0,0 @@
-import gradio as gr
-import librosa
-import soundfile
-import tempfile
-import os
-import uuid
-import json
-
-import jieba
-
-import nemo.collections.asr as nemo_asr
-from nemo.collections.asr.models import ASRModel
-from nemo.utils import logging
-
-from align import main, AlignmentConfig, ASSFileConfig
-
-
-SAMPLE_RATE = 16000
-
-# Pre-download and cache the model in disk space
-logging.setLevel(logging.ERROR)
-for tmp_model_name in [
-	"stt_en_fastconformer_hybrid_large_pc",
-	"stt_de_fastconformer_hybrid_large_pc",
-	"stt_es_fastconformer_hybrid_large_pc",
-	"stt_fr_conformer_ctc_large",
-	"stt_zh_citrinet_1024_gamma_0_25",
-]:
-	tmp_model = ASRModel.from_pretrained(tmp_model_name, map_location='cpu')
-	del tmp_model
-logging.setLevel(logging.INFO)
-
-
-def get_audio_data_and_duration(file):
-	data, sr = librosa.load(file)
-
-	if sr != SAMPLE_RATE:
-		data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
-
-	# monochannel
-	data = librosa.to_mono(data)
-
-	duration = librosa.get_duration(y=data, sr=SAMPLE_RATE)
-	return data, duration
-
-
-def get_char_tokens(text, model):
-	tokens = []
-	for character in text:
-		if character in model.decoder.vocabulary:
-			tokens.append(model.decoder.vocabulary.index(character))
-	else:
-		tokens.append(len(model.decoder.vocabulary))  # return unk token (same as blank token)
-
-	return tokens
-
-
-def get_S_prime_and_T(text, model_name, model, audio_duration):
-
-	# estimate T
-	if "citrinet" in model_name or "_fastconformer_" in model_name:
-		output_timestep_duration = 0.08
-	elif "_conformer_" in model_name:
-		output_timestep_duration = 0.04
-	elif "quartznet" in model_name:
-		output_timestep_duration = 0.02
-	else:
-		raise RuntimeError("unexpected model name")
-
-	T = int(audio_duration / output_timestep_duration) + 1
-
-	# calculate S_prime =  num tokens + num repetitions
-	if hasattr(model, 'tokenizer'):
-		all_tokens = model.tokenizer.text_to_ids(text)
-	elif hasattr(model.decoder, "vocabulary"):  # i.e. tokenization is simply character-based
-		all_tokens = get_char_tokens(text, model)
-	else:
-		raise RuntimeError("cannot obtain tokens from this model")
-
-	n_token_repetitions = 0
-	for i_tok in range(1, len(all_tokens)):
-		if all_tokens[i_tok] == all_tokens[i_tok - 1]:
-			n_token_repetitions += 1
-
-	S_prime = len(all_tokens) + n_token_repetitions
-
-	return S_prime, T
-
-
-def hex_to_rgb_list(hex_string):
-	hex_string = hex_string.lstrip("#")
-	r = int(hex_string[:2], 16)
-	g = int(hex_string[2:4], 16)
-	b = int(hex_string[4:], 16)
-	return [r, g, b]
-
-def delete_mp4s_except_given_filepath(filepath):
-	files_in_dir = os.listdir()
-	mp4_files_in_dir = [x for x in files_in_dir if x.endswith(".mp4")]
-	for mp4_file in mp4_files_in_dir:
-		if mp4_file != filepath:
-			os.remove(mp4_file)
-
-
-
-
-def align(lang, Microphone, File_Upload, text, col1, col2, col3, split_on_newline, progress=gr.Progress()):
-	# Create utt_id,  specify output_video_filepath and delete any MP4s
-	# that are not that filepath. These stray MP4s can be created
-	# if a user refreshes or exits the page while this 'align' function is executing.
-	# This deletion will not delete any other users' video as long as this 'align' function
-	# is run one at a time.
-	utt_id = uuid.uuid4()
-	output_video_filepath = f"{utt_id}.mp4"
-	delete_mp4s_except_given_filepath(output_video_filepath)
-
-	output_info = ""
-    ass_text = ""
-
-	progress(0, desc="Validating input")
-
-	# choose model
-	if lang in ["en", "de", "es"]:
-		model_name = f"stt_{lang}_fastconformer_hybrid_large_pc"
-	elif lang in ["fr"]:
-		model_name = f"stt_{lang}_conformer_ctc_large"
-	elif lang in ["zh"]:
-		model_name = f"stt_{lang}_citrinet_1024_gamma_0_25"
-
-	# decide which of Mic / File_Upload is used as input & do error handling
-	if (Microphone is not None) and (File_Upload is not None):
-		raise gr.Error("Please use either the microphone or file upload input - not both")
-
-	elif (Microphone is None) and (File_Upload is None):
-		raise gr.Error("You have to either use the microphone or upload an audio file")
-
-	elif Microphone is not None:
-		file = Microphone
-	else:
-		file = File_Upload
-
-	# check audio is not too long
-	audio_data, duration = get_audio_data_and_duration(file)
-
-	if duration > 4 * 60:
-		raise gr.Error(
-			f"Detected that uploaded audio has duration {duration/60:.1f} mins - please only upload audio of less than 4 mins duration"
-		)
-
-	# loading model
-	progress(0.1, desc="Loading speech recognition model")
-	model = ASRModel.from_pretrained(model_name)
-
-	if text:  # check input text is not too long compared to audio
-		S_prime, T = get_S_prime_and_T(text, model_name, model, duration)
-
-		if S_prime > T:
-			raise gr.Error(
-				f"The number of tokens in the input text is too long compared to the duration of the audio."
-				f" This model can handle {T} tokens + token repetitions at most. You have provided {S_prime} tokens + token repetitions. "
-				f" (Adjacent tokens that are not in the model's vocabulary are also counted as a token repetition.)"
-			)
-
-	with tempfile.TemporaryDirectory() as tmpdir:
-		audio_path = os.path.join(tmpdir, f'{utt_id}.wav')
-		soundfile.write(audio_path, audio_data, SAMPLE_RATE)
-
-		# getting the text if it hasn't been provided
-		if not text:
-			progress(0.2, desc="Transcribing audio")
-			text = model.transcribe([audio_path])[0]
-			if 'hybrid' in model_name:
-				text = text[0]
-
-			if text == "":
-				raise gr.Error(
-					"ERROR: the ASR model did not detect any speech in the input audio. Please upload audio with speech."
-				)
-
-			output_info += (
-				"You did not enter any input text, so the ASR model's transcription will be used:\n"
-				"--------------------------\n"
-				f"{text}\n"
-				"--------------------------\n"
-				f"You could try pasting the transcription into the text input box, correcting any"
-				" transcription errors, and clicking 'Submit' again."
-			)
-
-		if lang == "zh" and " " not in text:
-			# use jieba to add spaces between zh characters
-			text = " ".join(jieba.cut(text))
-
-		data = {
-			"audio_filepath": audio_path,
-			"text": text,
-		}
-		manifest_path = os.path.join(tmpdir, f"{utt_id}_manifest.json")
-		with open(manifest_path, 'w') as fout:
-			fout.write(f"{json.dumps(data)}\n")
-
-        # split text on new lines if requested
-        if split_on_newline:
-            text = "|".join(list(filter(None, text.split("\n"))))
-
-		# run alignment
-		if "|" in text:
-			resegment_text_to_fill_space = False
-		else:
-			resegment_text_to_fill_space = True
-
-		alignment_config = AlignmentConfig(
-			pretrained_name=model_name,
-			manifest_filepath=manifest_path,
-			output_dir=f"{tmpdir}/nfa_output/",
-			audio_filepath_parts_in_utt_id=1,
-			batch_size=1,
-			use_local_attention=True,
-			additional_segment_grouping_separator="|",
-			# transcribe_device='cpu',
-			# viterbi_device='cpu',
-			save_output_file_formats=["ass"],
-			ass_file_config=ASSFileConfig(
-				fontsize=45,
-				resegment_text_to_fill_space=resegment_text_to_fill_space,
-				max_lines_per_segment=4,
-				text_already_spoken_rgb=hex_to_rgb_list(col1),
-				text_being_spoken_rgb=hex_to_rgb_list(col2),
-				text_not_yet_spoken_rgb=hex_to_rgb_list(col3),
-			),
-		)
-
-		progress(0.5, desc="Aligning audio")
-
-		main(alignment_config)
-
-		progress(0.95, desc="Saving generated alignments")
-
-
-		if lang=="zh":
-			# make video file from the token-level ASS file
-			ass_file_for_video = f"{tmpdir}/nfa_output/ass/tokens/{utt_id}.ass"
-		else:
-			# make video file from the word-level ASS file
-			ass_file_for_video = f"{tmpdir}/nfa_output/ass/words/{utt_id}.ass"
-
-        with open(ass_file_for_video, "r") as ass_file:
-            ass_text = ass_file.read()
-
-		ffmpeg_command = (
-			f"ffmpeg -y -i {audio_path} "
-			"-f lavfi -i color=c=white:s=1280x720:r=50 "
-			"-crf 1 -shortest -vcodec libx264 -pix_fmt yuv420p "
-			f"-vf 'ass={ass_file_for_video}' "
-			f"{output_video_filepath}"
-		)
-
-		os.system(ffmpeg_command)
-
-	return output_video_filepath, gr.update(value=output_info, visible=True), output_video_filepath, ass_text
-
-
-def delete_non_tmp_video(video_path):
-	if video_path:
-		if os.path.exists(video_path):
-			os.remove(video_path)
-	return None
-
-
-with gr.Blocks(title="NeMo Forced Aligner", theme="huggingface") as demo:
-	non_tmp_output_video_filepath = gr.State([])
-
-	with gr.Row():
-		with gr.Column():
-			gr.Markdown("# NeMo Forced Aligner")
-			gr.Markdown(
-				"Demo for [NeMo Forced Aligner](https://github.com/NVIDIA/NeMo/tree/main/tools/nemo_forced_aligner) (NFA). "
-				"Upload audio and (optionally) the text spoken in the audio to generate a video where each part of the text will be highlighted as it is spoken. ",
-			)
-
-	with gr.Row():
-
-		with gr.Column(scale=1):
-			gr.Markdown("## Input")
-			lang_drop = gr.Dropdown(choices=["de", "en", "es", "fr", "zh"], value="en", label="Audio language",)
-
-			mic_in = gr.Audio(source="microphone", type='filepath', label="Microphone input (max 4 mins)")
-			audio_file_in = gr.Audio(source="upload", type='filepath', label="File upload (max 4 mins)")
-			ref_text = gr.Textbox(
-				label="[Optional] The reference text. Use '|' separators to specify which text will appear together. "
-				"Leave this field blank to use an ASR model's transcription as the reference text instead."
-			)
-            split_on_newline = gr.Checkbox(
-                label="Separate text on new lines", default=False
-            )
-
-			gr.Markdown("[Optional] For fun - adjust the colors of the text in the output video")
-			with gr.Row():
-				col1 = gr.ColorPicker(label="text already spoken", value="#fcba03")
-				col2 = gr.ColorPicker(label="text being spoken", value="#bf45bf")
-				col3 = gr.ColorPicker(label="text to be spoken", value="#3e1af0")
-
-			submit_button = gr.Button("Submit")
-
-		with gr.Column(scale=1):
-			gr.Markdown("## Output")
-			video_out = gr.Video(label="output video")
-			text_out = gr.Textbox(label="output info", visible=False)
-            ass_out = gr.Textbox(label="output .ass")
-
-	with gr.Row():
-		gr.HTML(
-			"<p style='text-align: center'>"
-				"Tutorial: <a href='https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/tools/NeMo_Forced_Aligner_Tutorial.ipynb' target='_blank'>\"How to use NFA?\"</a> 🚀 | "
-				"Blog post: <a href='https://nvidia.github.io/NeMo/blogs/2023/2023-08-forced-alignment/' target='_blank'>\"How does forced alignment work?\"</a> 📚 | "
-				"NFA <a href='https://github.com/NVIDIA/NeMo/tree/main/tools/nemo_forced_aligner/' target='_blank'>Github page</a> 👩‍💻"
-			"</p>"
-		)
-
-	submit_button.click(
-		fn=align,
-		inputs=[lang_drop, mic_in, audio_file_in, ref_text, col1, col2, col3,split_on_newline,],
-		outputs=[video_out, text_out, non_tmp_output_video_filepath, ass_out],
-	).then(
-		fn=delete_non_tmp_video, inputs=[non_tmp_output_video_filepath], outputs=None,
-	)
-
-demo.queue()
-demo.launch()
-