Remove `html_content`

app.py

@@ -1,84 +1,83 @@
 # coding=utf-8
 
-import os
-import librosa
 import base64
 import io
-import gradio as gr
+import os
 import re
 
+import gradio as gr
+import librosa
 import numpy as np
+import spaces
 import torch
 import torchaudio
-
-import spaces
-
 from funasr import AutoModel
 
 model = "FunAudioLLM/SenseVoiceSmall"
-model = AutoModel(model=model,
-				  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-				  vad_kwargs={"max_single_segment_time": 30000},
-                  hub="hf",
-                  device="cuda"
-				  )
+model = AutoModel(
+    model=model,
+    vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    vad_kwargs={"max_single_segment_time": 30000},
+    hub="hf",
+    device="cuda",
+)
 
 import re
 
 emo_dict = {
-	"<|HAPPY|>": "😊",
-	"<|SAD|>": "😔",
-	"<|ANGRY|>": "😡",
-	"<|NEUTRAL|>": "",
-	"<|FEARFUL|>": "😰",
-	"<|DISGUSTED|>": "🤢",
-	"<|SURPRISED|>": "😮",
+    "<|HAPPY|>": "😊",
+    "<|SAD|>": "😔",
+    "<|ANGRY|>": "😡",
+    "<|NEUTRAL|>": "",
+    "<|FEARFUL|>": "😰",
+    "<|DISGUSTED|>": "🤢",
+    "<|SURPRISED|>": "😮",
 }
 
 event_dict = {
-	"<|BGM|>": "🎼",
-	"<|Speech|>": "",
-	"<|Applause|>": "👏",
-	"<|Laughter|>": "😀",
-	"<|Cry|>": "😭",
-	"<|Sneeze|>": "🤧",
-	"<|Breath|>": "",
-	"<|Cough|>": "🤧",
+    "<|BGM|>": "🎼",
+    "<|Speech|>": "",
+    "<|Applause|>": "👏",
+    "<|Laughter|>": "😀",
+    "<|Cry|>": "😭",
+    "<|Sneeze|>": "🤧",
+    "<|Breath|>": "",
+    "<|Cough|>": "🤧",
 }
 
 emoji_dict = {
-	"<|nospeech|><|Event_UNK|>": "❓",
-	"<|zh|>": "",
-	"<|en|>": "",
-	"<|yue|>": "",
-	"<|ja|>": "",
-	"<|ko|>": "",
-	"<|nospeech|>": "",
-	"<|HAPPY|>": "😊",
-	"<|SAD|>": "😔",
-	"<|ANGRY|>": "😡",
-	"<|NEUTRAL|>": "",
-	"<|BGM|>": "🎼",
-	"<|Speech|>": "",
-	"<|Applause|>": "👏",
-	"<|Laughter|>": "😀",
-	"<|FEARFUL|>": "😰",
-	"<|DISGUSTED|>": "🤢",
-	"<|SURPRISED|>": "😮",
-	"<|Cry|>": "😭",
-	"<|EMO_UNKNOWN|>": "",
-	"<|Sneeze|>": "🤧",
-	"<|Breath|>": "",
-	"<|Cough|>": "😷",
-	"<|Sing|>": "",
-	"<|Speech_Noise|>": "",
-	"<|withitn|>": "",
-	"<|woitn|>": "",
-	"<|GBG|>": "",
-	"<|Event_UNK|>": "",
+    "<|nospeech|><|Event_UNK|>": "❓",
+    "<|zh|>": "",
+    "<|en|>": "",
+    "<|yue|>": "",
+    "<|ja|>": "",
+    "<|ko|>": "",
+    "<|nospeech|>": "",
+    "<|HAPPY|>": "😊",
+    "<|SAD|>": "😔",
+    "<|ANGRY|>": "😡",
+    "<|NEUTRAL|>": "",
+    "<|BGM|>": "🎼",
+    "<|Speech|>": "",
+    "<|Applause|>": "👏",
+    "<|Laughter|>": "😀",
+    "<|FEARFUL|>": "😰",
+    "<|DISGUSTED|>": "🤢",
+    "<|SURPRISED|>": "😮",
+    "<|Cry|>": "😭",
+    "<|EMO_UNKNOWN|>": "",
+    "<|Sneeze|>": "🤧",
+    "<|Breath|>": "",
+    "<|Cough|>": "😷",
+    "<|Sing|>": "",
+    "<|Speech_Noise|>": "",
+    "<|withitn|>": "",
+    "<|woitn|>": "",
+    "<|GBG|>": "",
+    "<|Event_UNK|>": "",
 }
 
-lang_dict =  {
+lang_dict = {
     "<|zh|>": "<|lang|>",
     "<|en|>": "<|lang|>",
     "<|yue|>": "<|lang|>",
@@ -88,98 +87,111 @@ lang_dict =  {
 }
 
 emo_set = {"😊", "😔", "😡", "😰", "🤢", "😮"}
-event_set = {"🎼", "👏", "😀", "😭", "🤧", "😷",}
+event_set = {"🎼", "👏", "😀", "😭", "🤧", "😷"}
+
 
 def format_str(s):
-	for sptk in emoji_dict:
-		s = s.replace(sptk, emoji_dict[sptk])
-	return s
+    for sptk in emoji_dict:
+        s = s.replace(sptk, emoji_dict[sptk])
+    return s
 
 
 def format_str_v2(s):
-	sptk_dict = {}
-	for sptk in emoji_dict:
-		sptk_dict[sptk] = s.count(sptk)
-		s = s.replace(sptk, "")
-	emo = "<|NEUTRAL|>"
-	for e in emo_dict:
-		if sptk_dict[e] > sptk_dict[emo]:
-			emo = e
-	for e in event_dict:
-		if sptk_dict[e] > 0:
-			s = event_dict[e] + s
-	s = s + emo_dict[emo]
-
-	for emoji in emo_set.union(event_set):
-		s = s.replace(" " + emoji, emoji)
-		s = s.replace(emoji + " ", emoji)
-	return s.strip()
+    sptk_dict = {}
+    for sptk in emoji_dict:
+        sptk_dict[sptk] = s.count(sptk)
+        s = s.replace(sptk, "")
+    emo = "<|NEUTRAL|>"
+    for e in emo_dict:
+        if sptk_dict[e] > sptk_dict[emo]:
+            emo = e
+    for e in event_dict:
+        if sptk_dict[e] > 0:
+            s = event_dict[e] + s
+    s = s + emo_dict[emo]
+
+    for emoji in emo_set.union(event_set):
+        s = s.replace(" " + emoji, emoji)
+        s = s.replace(emoji + " ", emoji)
+    return s.strip()
+
 
 def format_str_v3(s):
-	def get_emo(s):
-		return s[-1] if s[-1] in emo_set else None
-	def get_event(s):
-		return s[0] if s[0] in event_set else None
-
-	s = s.replace("<|nospeech|><|Event_UNK|>", "❓")
-	for lang in lang_dict:
-		s = s.replace(lang, "<|lang|>")
-	s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")]
-	new_s = " " + s_list[0]
-	cur_ent_event = get_event(new_s)
-	for i in range(1, len(s_list)):
-		if len(s_list[i]) == 0:
-			continue
-		if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None:
-			s_list[i] = s_list[i][1:]
-		#else:
-		cur_ent_event = get_event(s_list[i])
-		if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s):
-			new_s = new_s[:-1]
-		new_s += s_list[i].strip().lstrip()
-	new_s = new_s.replace("The.", " ")
-	return new_s.strip()
+    def get_emo(s):
+        return s[-1] if s[-1] in emo_set else None
+
+    def get_event(s):
+        return s[0] if s[0] in event_set else None
+
+    s = s.replace("<|nospeech|><|Event_UNK|>", "❓")
+    for lang in lang_dict:
+        s = s.replace(lang, "<|lang|>")
+    s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")]
+    new_s = " " + s_list[0]
+    cur_ent_event = get_event(new_s)
+    for i in range(1, len(s_list)):
+        if len(s_list[i]) == 0:
+            continue
+        if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None:
+            s_list[i] = s_list[i][1:]
+        # else:
+        cur_ent_event = get_event(s_list[i])
+        if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s):
+            new_s = new_s[:-1]
+        new_s += s_list[i].strip().lstrip()
+    new_s = new_s.replace("The.", " ")
+    return new_s.strip()
+
 
 @spaces.GPU
 def model_inference(input_wav, language, fs=16000):
-	# task_abbr = {"Speech Recognition": "ASR", "Rich Text Transcription": ("ASR", "AED", "SER")}
-	language_abbr = {"auto": "auto", "zh": "zh", "en": "en", "yue": "yue", "ja": "ja", "ko": "ko",
-					 "nospeech": "nospeech"}
-	
-	# task = "Speech Recognition" if task is None else task
-	language = "auto" if len(language) < 1 else language
-	selected_language = language_abbr[language]
-	# selected_task = task_abbr.get(task)
-	
-	# print(f"input_wav: {type(input_wav)}, {input_wav[1].shape}, {input_wav}")
-	
-	if isinstance(input_wav, tuple):
-		fs, input_wav = input_wav
-		input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
-		if len(input_wav.shape) > 1:
-			input_wav = input_wav.mean(-1)
-		if fs != 16000:
-			print(f"audio_fs: {fs}")
-			resampler = torchaudio.transforms.Resample(fs, 16000)
-			input_wav_t = torch.from_numpy(input_wav).to(torch.float32)
-			input_wav = resampler(input_wav_t[None, :])[0, :].numpy()
-	
-	
-	merge_vad = True #False if selected_task == "ASR" else True
-	print(f"language: {language}, merge_vad: {merge_vad}")
-	text = model.generate(input=input_wav,
-						  cache={},
-						  language=language,
-						  use_itn=True,
-						  batch_size_s=500, merge_vad=merge_vad)
-	
-	print(text)
-	text = text[0]["text"]
-	text = format_str_v3(text)
-	
-	print(text)
-	
-	return text
+    # task_abbr = {"Speech Recognition": "ASR", "Rich Text Transcription": ("ASR", "AED", "SER")}
+    language_abbr = {
+        "auto": "auto",
+        "zh": "zh",
+        "en": "en",
+        "yue": "yue",
+        "ja": "ja",
+        "ko": "ko",
+        "nospeech": "nospeech",
+    }
+
+    # task = "Speech Recognition" if task is None else task
+    language = "auto" if len(language) < 1 else language
+    selected_language = language_abbr[language]
+    # selected_task = task_abbr.get(task)
+
+    # print(f"input_wav: {type(input_wav)}, {input_wav[1].shape}, {input_wav}")
+
+    if isinstance(input_wav, tuple):
+        fs, input_wav = input_wav
+        input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
+        if len(input_wav.shape) > 1:
+            input_wav = input_wav.mean(-1)
+        if fs != 16000:
+            print(f"audio_fs: {fs}")
+            resampler = torchaudio.transforms.Resample(fs, 16000)
+            input_wav_t = torch.from_numpy(input_wav).to(torch.float32)
+            input_wav = resampler(input_wav_t[None, :])[0, :].numpy()
+
+    merge_vad = True  # False if selected_task == "ASR" else True
+    print(f"language: {language}, merge_vad: {merge_vad}")
+    text = model.generate(
+        input=input_wav,
+        cache={},
+        language=language,
+        use_itn=True,
+        batch_size_s=500,
+        merge_vad=merge_vad,
+    )
+
+    print(text)
+    text = text[0]["text"]
+    text = format_str_v3(text)
+
+    print(text)
+
+    return text
 
 
 audio_examples = [
@@ -200,28 +212,34 @@ audio_examples = [
 
 
 def launch():
-	with gr.Blocks(theme=gr.themes.Soft()) as demo:
-		# gr.Markdown(description)
-		gr.HTML(html_content)
-		with gr.Row():
-			with gr.Column():
-				audio_inputs = gr.Audio(label="Upload audio or use the microphone")
-				
-				with gr.Accordion("Configuration"):
-					language_inputs = gr.Dropdown(choices=["auto", "zh", "en", "yue", "ja", "ko", "nospeech"],
-												  value="auto",
-												  label="Language")
-				fn_button = gr.Button("Start", variant="primary")
-				text_outputs = gr.Textbox(label="Results")
-			gr.Examples(examples=audio_examples, inputs=[audio_inputs, language_inputs], examples_per_page=20)
-		
-		fn_button.click(model_inference, inputs=[audio_inputs, language_inputs], outputs=text_outputs)
-
-	demo.launch()
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column():
+                audio_inputs = gr.Audio(label="Upload audio or use the microphone")
+
+                with gr.Accordion("Configuration"):
+                    language_inputs = gr.Dropdown(
+                        choices=["auto", "zh", "en", "yue", "ja", "ko", "nospeech"],
+                        value="auto",
+                        label="Language",
+                    )
+                fn_button = gr.Button("Start", variant="primary")
+                text_outputs = gr.Textbox(label="Results")
+            gr.Examples(
+                examples=audio_examples,
+                inputs=[audio_inputs, language_inputs],
+                examples_per_page=20,
+            )
+
+        fn_button.click(
+            model_inference,
+            inputs=[audio_inputs, language_inputs],
+            outputs=text_outputs,
+        )
+
+    demo.launch()
 
 
 if __name__ == "__main__":
-	# iface.launch()
-	launch()
-
-
+    # iface.launch()
+    launch()