Add bi_v100-sensevoice and bi_v100-whisper as subdirectories

bi_v100-whisper/Dockerfile_whisper

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+FROM zibo.harbor.iluvatar.com.cn:30000/saas/bi100-3.2.1-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.2
+
+RUN mkdir /workspace
+WORKDIR /workspace/
+
+COPY asr_server_whisper.py /workspace/asr_server_whisper.py
+COPY ./launch_service /workspace/launch_service
+COPY whisper-tiny /model
+
+ENTRYPOINT ["./launch_service"]

bi_v100-whisper/asr_server_whisper.py

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+import torch
+from transformers import pipeline
+from flask import Flask, request, jsonify
+from threading import Lock
+
+asr_pipeline = pipeline(
+    task="automatic-speech-recognition",
+    model="/model",
+    torch_dtype=torch.float16,
+    device="cuda:0"
+)
+
+app = Flask(__name__)
+pipeline_lock = Lock()
+
+@app.route('/recognition', methods=['POST'])
+def predict():
+    audio_data = request.data
+    params = request.params
+    params_json = json.loads(params)
+    lang = params_json.get('language', 'en')
+    with pipeline_lock:
+        res = asr_pipeline(inputs=audio_data, generate_kwargs={"language": lang})
+    text = res['text'].strip()
+    return jsonify({'RecognitionStatus': 'Success', "DisplayText": text})
+
+@app.route('/ready', methods=['GET'])
+@app.route('/health', methods=['GET'])
+@app.route('/health_check', methods=['GET'])
+def health():
+    return jsonify({'status': 'ok'})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=80)

bi_v100-whisper/launch_service

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+#!/bin/bash
+
+data
+cat /proc/cpuinfo
+ixsmi
+export
+date
+
+python3 asr.py

bi_v100-whisper/whisper-tiny/.mv

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+Revision:master,CreatedAt:1736300537