Initial version

README.md

@@ -1,12 +1,12 @@
 ---
-title: Asr
+title: Automatic Speech Recognition
 emoji: 🌍
-colorFrom: yellow
-colorTo: yellow
+colorFrom: magenta
+colorTo: magenta
 sdk: gradio
 sdk_version: 3.0.26
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
 ---
 

app.py

@@ -0,0 +1,116 @@
+import logging
+import sys
+import gradio as gr
+import vosk
+import json
+import subprocess
+
+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 = {
+    "Russian": {"model_id": "vosk-model-ru-0.22"},
+    "Chinese": {"model_id": "vosk-model-cn-0.22"},
+    "English": {"model_id": "vosk-model-en-us-0.22"},
+    "French": {"model_id": "vosk-model-fr-0.22"},
+    "German": {"model_id": "vosk-model-de-0.22"},
+    "Italian": {"model_id": "vosk-model-it-0.22"},
+    "Japanese": {"model_id": "vosk-model-ja-0.22"},
+    "Persian": {"model_id": "vosk-model-fa-0.5"},
+}
+
+LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
+CACHED_MODELS_BY_ID = {}
+
+def asr(model, input_file):
+
+    rec = vosk.KaldiRecognizer(model, 16000.0)
+    results = []
+
+    process = subprocess.Popen(f'ffmpeg -loglevel quiet -i {input_file} -ar 16000 -ac 1 -f s16le -'.split(),
+                            stdout=subprocess.PIPE)
+
+    while True:
+        data = process.stdout.read(4000)
+        if len(data) == 0:
+            break
+        if rec.AcceptWaveform(data):
+            jres = json.loads(rec.Result())
+            results.append(jres['text'])
+
+    jres = json.loads(rec.FinalResult())
+    results.append(jres['text'])
+
+    return " ".join(results)
+
+
+def run(input_file, language, history):
+
+    logger.info(f"Running ASR for {language} for {input_file}")
+
+    history = history or []
+
+    model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
+
+    if model is None:
+        history.append({
+            "error_message": f"Failed to find a model for {language} language :("
+        })
+    else:
+        model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
+        if model_instance is None:
+            model_instance = vosk.Model(model_name=model["model_id"])
+            CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
+
+        transcription = asr(model_instance, input_file.name)
+
+        logger.info(f"Transcription for {input_file}: {transcription}")
+
+        history.append({
+            "model_id": model["model_id"],
+            "language": language,
+            "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:
+            html_output += "<div class='result_item result_item_success'>"
+            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="file", label="Record something..."),
+        gr.inputs.Radio(label="Language", choices=LANGUAGES),
+        "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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+vosk