Update app.py

app.py

@@ -1,187 +1,187 @@
-from nemo.collections.asr.models import EncDecCTCModelBPE
-from omegaconf import open_dict
-#import yt_dlp as youtube_dl
-import os
-import tempfile
-import torch
-import gradio as gr
-from pydub import AudioSegment
-import time
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-MODEL_NAME="ayymen/stt_zgh_fastconformer_ctc_small"
-YT_LENGTH_LIMIT_S=3600
-
-model = EncDecCTCModelBPE.from_pretrained(model_name=MODEL_NAME).to(device)
-
-with open_dict(model.cfg):
-    model.cfg.decoding.strategy = "beam"
-    model.cfg.decoding.beam.beam_size = 256 # Desired Beam Size
-    model.cfg.decoding.beam.beam_alpha = 1.5 # Desired Beam Alpha
-    model.cfg.decoding.beam.beam_beta = 1.5 # Desired Beam Beta
-    model.cfg.decoding.beam.kenlm_path = "kenlm.bin" # Path to KenLM binary file
-
-model.change_decoding_strategy(model.cfg.decoding)
-
-model.eval()
-
-def get_transcripts(audio_path):
-    audio = AudioSegment.from_file(audio_path)
-    # check if audio is mono 16kHz
-    if audio.channels != 1 or audio.frame_rate != 16000:
-        audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            audio_path = os.path.join(tmpdirname, "audio.wav")
-            audio.export(audio_path, format="wav")
-            text = model.transcribe([audio_path])[0]
-    else:
-        text = model.transcribe([audio_path])[0]
-    return text
-
-'''
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://huggingface.co/nvidia/parakeet-rnnt-1.1b' target='_blank'>🎙️ Learn more about Parakeet model</a> | "
-    "<a href='https://arxiv.org/abs/2305.05084' target='_blank'>📚 FastConformer paper</a> | "
-    "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑‍💻 Repository</a>"
-    "</p>"
-)
-'''
-
-EXAMPLES = [
-    ["135.wav"],
-    ["common_voice_zgh_37837257.mp3"],
-]
-
-"""
-YT_EXAMPLES = [
-    ["https://www.youtube.com/shorts/CSgTSE50MHY"],
-    ["https://www.youtube.com/shorts/OxQtqOyAFLE"]
-]
-"""
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    if "youtube.com/shorts/" in video_id:
-        video_id = video_id.split("/")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-
-def yt_transcribe(yt_url, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "video.mp4")
-        download_yt_audio(yt_url, filepath)
-        audio = AudioSegment.from_file(filepath)
-        audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
-        wav_filepath = os.path.join(tmpdirname, "audio.wav")
-        audio.export(wav_filepath, format="wav")
-        text = get_transcripts(wav_filepath)
-
-    return html_embed_str, text
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=get_transcripts,
-    inputs=[
-        gr.Audio(sources="microphone", type="filepath")
-    ],
-    outputs="text",
-    title="Transcribe Audio",
-    description=(
-        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-    cache_examples=False,
-)
-
-file_transcribe = gr.Interface(
-    fn=get_transcripts,
-    inputs=[
-        gr.Audio(sources="upload", type="filepath", label="Audio file"),
-    ],
-    outputs="text",
-    examples=EXAMPLES,
-    title="Transcribe Audio",
-    description=(
-        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-    cache_examples=False,
-)
-
-"""
-youtube_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-    ],
-    outputs=["html", "text"],
-    examples=YT_EXAMPLES,
-    title="Transcribe Audio",
-    description=(
-        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-"""
-
-with demo:
-    gr.TabbedInterface(
-        [
-            mf_transcribe,
-            file_transcribe,
-            #youtube_transcribe
-        ],
-        [
-            "Microphone",
-            "Audio file",
-            #"Youtube Video"
-        ]
-    )
-
-demo.launch()
+from nemo.collections.asr.models import EncDecCTCModelBPE
+from omegaconf import open_dict
+#import yt_dlp as youtube_dl
+import os
+import tempfile
+import torch
+import gradio as gr
+from pydub import AudioSegment
+import time
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL_NAME="ayymen/stt_zgh_fastconformer_ctc_small"
+YT_LENGTH_LIMIT_S=3600
+
+model = EncDecCTCModelBPE.from_pretrained(model_name=MODEL_NAME).to(device)
+
+with open_dict(model.cfg):
+    model.cfg.decoding.strategy = "beam"
+    model.cfg.decoding.beam.beam_size = 256 # Desired Beam Size
+    model.cfg.decoding.beam.beam_alpha = 1.5 # Desired Beam Alpha
+    model.cfg.decoding.beam.beam_beta = 1.5 # Desired Beam Beta
+    model.cfg.decoding.beam.kenlm_path = "kenlm.bin" # Path to KenLM binary file
+
+model.change_decoding_strategy(model.cfg.decoding)
+
+model.eval()
+
+def get_transcripts(audio_path):
+    audio = AudioSegment.from_file(audio_path)
+    # check if audio is mono 16kHz
+    if audio.channels != 1 or audio.frame_rate != 16000:
+        audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            audio_path = os.path.join(tmpdirname, "audio.wav")
+            audio.export(audio_path, format="wav")
+            text = model.transcribe([audio_path])[0]
+    else:
+        text = model.transcribe([audio_path])[0]
+    return text
+
+'''
+article = (
+    "<p style='text-align: center'>"
+    "<a href='https://huggingface.co/nvidia/parakeet-rnnt-1.1b' target='_blank'>🎙️ Learn more about Parakeet model</a> | "
+    "<a href='https://arxiv.org/abs/2305.05084' target='_blank'>📚 FastConformer paper</a> | "
+    "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑‍💻 Repository</a>"
+    "</p>"
+)
+'''
+
+EXAMPLES = [
+    ["135.wav"],
+    ["common_voice_zgh_37837257.mp3"],
+]
+
+"""
+YT_EXAMPLES = [
+    ["https://www.youtube.com/shorts/CSgTSE50MHY"],
+    ["https://www.youtube.com/shorts/OxQtqOyAFLE"]
+]
+"""
+
+def _return_yt_html_embed(yt_url):
+    video_id = yt_url.split("?v=")[-1]
+    if "youtube.com/shorts/" in video_id:
+        video_id = video_id.split("/")[-1]
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
+    )
+    return HTML_str
+
+def download_yt_audio(yt_url, filename):
+    info_loader = youtube_dl.YoutubeDL()
+    
+    try:
+        info = info_loader.extract_info(yt_url, download=False)
+    except youtube_dl.utils.DownloadError as err:
+        raise gr.Error(str(err))
+    
+    file_length = info["duration_string"]
+    file_h_m_s = file_length.split(":")
+    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
+    
+    if len(file_h_m_s) == 1:
+        file_h_m_s.insert(0, 0)
+    if len(file_h_m_s) == 2:
+        file_h_m_s.insert(0, 0)
+    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
+    
+    if file_length_s > YT_LENGTH_LIMIT_S:
+        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
+        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
+        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
+    
+    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
+    
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        try:
+            ydl.download([yt_url])
+        except youtube_dl.utils.ExtractorError as err:
+            raise gr.Error(str(err))
+
+
+def yt_transcribe(yt_url, max_filesize=75.0):
+    html_embed_str = _return_yt_html_embed(yt_url)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        filepath = os.path.join(tmpdirname, "video.mp4")
+        download_yt_audio(yt_url, filepath)
+        audio = AudioSegment.from_file(filepath)
+        audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
+        wav_filepath = os.path.join(tmpdirname, "audio.wav")
+        audio.export(wav_filepath, format="wav")
+        text = get_transcripts(wav_filepath)
+
+    return html_embed_str, text
+
+
+demo = gr.Blocks()
+
+mf_transcribe = gr.Interface(
+    fn=get_transcripts,
+    inputs=[
+        gr.Audio(sources="microphone", type="filepath")
+    ],
+    outputs="text",
+    title="Transcribe Audio",
+    description=(
+        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+    cache_examples=False,
+)
+
+file_transcribe = gr.Interface(
+    fn=get_transcripts,
+    inputs=[
+        gr.Audio(sources="upload", type="filepath", label="Audio file"),
+    ],
+    outputs="text",
+    examples=EXAMPLES,
+    title="Transcribe Audio",
+    description=(
+        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+    cache_examples=False,
+)
+
+"""
+youtube_transcribe = gr.Interface(
+    fn=yt_transcribe,
+    inputs=[
+        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+    ],
+    outputs=["html", "text"],
+    examples=YT_EXAMPLES,
+    title="Transcribe Audio",
+    description=(
+        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
+        " of arbitrary length."
+    ),
+    allow_flagging="never",
+)
+"""
+
+with demo:
+    gr.TabbedInterface(
+        [
+            mf_transcribe,
+            file_transcribe,
+            #youtube_transcribe
+        ],
+        [
+            "Microphone",
+            "Audio file",
+            #"Youtube Video"
+        ]
+    )
+
+demo.launch(server_name="0.0.0.0")