Adding app files

README.md

@@ -1,5 +1,5 @@
 ---
-title: LatvianSpeechRecognition
+title: Latvian Speech Recognition
 emoji: 🦀
 colorFrom: green
 colorTo: indigo
@@ -7,6 +7,13 @@ sdk: gradio
 sdk_version: 4.28.3
 app_file: app.py
 pinned: false
+preload_from_hub: 
+  - RaivisDejus/whisper-tiny-lv
+  - RaivisDejus/whisper-small-lv
+  - AiLab-IMCS-UL/whisper-large-v3-lv-late-cv17
+tags:
+  - latvian
+  - whisper
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,156 @@
+import torch
+import time
+
+import gradio as gr
+import yt_dlp as youtube_dl
+from transformers import pipeline
+from transformers.pipelines.audio_utils import ffmpeg_read
+
+import tempfile
+import os
+
+BATCH_SIZE = 8
+FILE_LIMIT_MB = 1000
+YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+
+def transcribe(model, audio, task):
+    if audio is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+
+    pipe = pipeline(
+        task="automatic-speech-recognition",
+        model=model,
+        chunk_length_s=30,
+        device=device,
+    )
+    text = pipe(audio, batch_size=BATCH_SIZE, generate_kwargs={"language": "latvian", "task": task}, return_timestamps=True)["text"]
+    return text
+
+
+def _return_yt_html_embed(yt_url):
+    video_id = yt_url.split("?v=")[-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(model, yt_url, task):
+    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)
+        with open(filepath, "rb") as f:
+            inputs = f.read()
+
+    pipe = pipeline(
+        task="automatic-speech-recognition",
+        model=model,
+        chunk_length_s=30,
+        device=device,
+    )
+    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
+    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
+
+    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"language": "latvian", "task": task}, return_timestamps=True)["text"]
+
+    return html_embed_str, text
+
+
+demo = gr.Blocks()
+
+transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        # gr.Markdown(
+        #     """
+        #     Test Latvian speech recognition (STT) models. Three models are available:
+        #     * [tiny](https://huggingface.co/RaivisDejus/whisper-tiny-lv) - Fastest, requiring least RAM, but also least accurate
+        #     * [small](https://huggingface.co/RaivisDejus/whisper-small-lv) - Reasonably fast, reasonably accurate, requiring reasonable amounts of RAM
+        #     * [large](https://huggingface.co/AiLab-IMCS-UL/whisper-large-v3-lv-late-cv17) - Most accurate, developed by scientists from [ailab.lv](https://ailab.lv/). Requires most RAM and for best performance should be run on a GPU.
+        #     """
+        # ),
+        gr.Dropdown([
+            ("tiny", "RaivisDejus/whisper-tiny-lv"),
+            ("small", "RaivisDejus/whisper-small-lv"),
+            ("large", "AiLab-IMCS-UL/whisper-large-v3-lv-late-cv17")
+        ], label="Model", value="RaivisDejus/whisper-small-lv"),
+        gr.Audio(sources=["upload", "microphone"],type="filepath", label="Audio"),
+        gr.Radio([("Transcribe", "transcribe"), ("Translate to English", "translate",)], label="Task", value="transcribe"),
+    ],
+    outputs=gr.Textbox(label="Transcription", lines=10),
+    title="Latvian speech recognition: Transcribe Audio",
+    description=("""
+        Test Latvian speech recognition (STT) models. Three models are available:
+        
+        * [tiny](https://huggingface.co/RaivisDejus/whisper-tiny-lv) - Fastest, requiring least RAM, but also least accurate
+        
+        * [small](https://huggingface.co/RaivisDejus/whisper-small-lv) - Reasonably fast, reasonably accurate, requiring reasonable amounts of RAM
+        
+        * [large](https://huggingface.co/AiLab-IMCS-UL/whisper-large-v3-lv-late-cv17) - Most accurate, developed by scientists from [ailab.lv](https://ailab.lv/). Requires most RAM and for best performance should be run on a GPU.
+        """
+    ),
+    allow_flagging="never",
+)
+
+yt_transcribe = gr.Interface(
+    fn=yt_transcribe,
+    inputs=[
+        gr.Dropdown([
+            ("tiny", "RaivisDejus/whisper-tiny-lv"),
+            ("small", "RaivisDejus/whisper-small-lv"),
+            ("large", "AiLab-IMCS-UL/whisper-large-v3-lv-late-cv17")
+        ], label="Model", value="RaivisDejus/whisper-small-lv"),
+        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+        gr.Radio([("Transcribe", "transcribe"), ("Translate to English", "translate",)], label="Task", value="transcribe")
+    ],
+    outputs=["html", "text"],
+    title="Latvian speech recognition: Transcribe YouTube",
+    description=(
+        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
+    ),
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface([transcribe, yt_transcribe], ["Microphone / Audio file", "YouTube"])
+
+demo.queue(max_size=10)
+demo.launch()
+

packages.txt

@@ -0,0 +1 @@
+ffmpeg

requirements.txt

@@ -0,0 +1,3 @@
+git+https://github.com/huggingface/transformers
+torch
+yt-dlp