add application file

app.py

@@ -0,0 +1,116 @@
+import logging
+import sys
+import gradio as gr
+from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+LARGE_MODEL_BY_LANGUAGE = {
+    "Korean": {"model_id": "kresnik/wav2vec2-large-xlsr-korean", "has_lm": True},
+}
+
+
+# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
+
+# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
+LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
+CACHED_MODELS_BY_ID = {}
+
+
+def run(input_file, language, decoding_type, history, model_size="300M"):
+
+    logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")
+
+    history = history or []
+
+    if model_size == "300M":
+        model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
+    else:
+        model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)
+
+    if model is None:
+        history.append({
+            "error_message": f"Model size {model_size} not found for {language} language :("
+        })
+    elif decoding_type == "LM" and not model["has_lm"]:
+        history.append({
+            "error_message": f"LM not available for {language} language :("
+        })
+    else:
+
+        # model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
+        model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
+        if model_instance is None:
+            model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
+            CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
+
+        if decoding_type == "LM":
+            processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
+            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
+                           feature_extractor=processor.feature_extractor, decoder=processor.decoder)
+        else:
+            processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
+            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
+                           feature_extractor=processor.feature_extractor, decoder=None)
+
+        transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]
+
+        logger.info(f"Transcription for {input_file}: {transcription}")
+
+        history.append({
+            "model_id": model["model_id"],
+            "language": language,
+            "model_size": model_size,
+            "decoding_type": decoding_type,
+            "transcription": transcription,
+            "error_message": None
+        })
+
+    html_output = "<div class='result'>"
+    for item in history:
+        if item["error_message"] is not None:
+            html_output += f"<div class='result_item result_item_error'>{item['error_message']}</div>"
+        else:
+            url_suffix = " + LM" if item["decoding_type"] == "LM" else ""
+            html_output += "<div class='result_item result_item_success'>"
+            html_output += f'<strong><a target="_blank" href="https://huggingface.co/{item["model_id"]}">{item["model_id"]}{url_suffix}</a></strong><br/><br/>'
+            html_output += f'{item["transcription"]}<br/>'
+            html_output += "</div>"
+    html_output += "</div>"
+
+    return html_output, history
+
+
+gr.Interface(
+    run,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
+        gr.inputs.Radio(label="Language", choices=LANGUAGES),
+        gr.inputs.Radio(label="Decoding type", choices=["greedy"]),
+        # gr.inputs.Radio(label="Model size", choices=["300M", "1B"]),
+        "state"
+    ],
+    outputs=[
+        gr.outputs.HTML(label="Outputs"),
+        "state"
+    ],
+    title="Automatic Speech Recognition",
+    description="",
+    css="""
+    .result {display:flex;flex-direction:column}
+    .result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
+    .result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
+    .result_item_error {background-color:#ff7070;color:white;align-self:start}
+    """,
+    allow_screenshot=False,
+    allow_flagging="never",
+    theme="grass"
+).launch(enable_queue=True)
+

packages.txt

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+ffmpeg
+

requirements.txt

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+transformers
+torch
+pyctcdecode
+pypi-kenlm
+